Adds 4 Missing Models

brainscore_vision/models/efficientnet_b0/__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['efficientnet_b0'] = lambda: ModelCommitment(identifier='efficientnet_b0',
+                                                                    activations_model=get_model('efficientnet_b0'),
+                                                                    layers=get_layers('efficientnet_b0'))

brainscore_vision/models/efficientnet_b0/model.py

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+import functools
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.check_submission import check_models
+import torchvision.models
+
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+def get_model(name):
+    assert name == 'efficientnet_b0'
+    model = torchvision.models.efficientnet_b0(pretrained=True)
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    wrapper = PytorchWrapper(identifier='efficientnet_b0', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+def get_layers(name):
+    assert name == 'efficientnet_b0'
+    return ['features.0.2',
+             'features.2.1.stochastic_depth',
+             'features.3.1.stochastic_depth',
+             'features.4.1.stochastic_depth',
+             'features.4.2.stochastic_depth',
+             'features.5.1.stochastic_depth',
+             'features.5.2.stochastic_depth',
+             'features.6.1.stochastic_depth',
+             'features.6.2.stochastic_depth',
+             'features.8.2','classifier.0','classifier.1',
+             ]
+
+
+def get_bibtex(model_identifier):
+    return """@misc{tan2020efficientnet,
+      title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, 
+      author={Mingxing Tan and Quoc V. Le},
+      year={2020},
+      eprint={1905.11946},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/efficientnet_b0/region_layer_map/efficientnet_b0.json

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+{"IT": "features.6.1.stochastic_depth", "V4": "features.4.1.stochastic_depth", "V1": "features.3.1.stochastic_depth", "V2": "features.6.1.stochastic_depth"}

brainscore_vision/models/efficientnet_b0/requirements.txt

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+torch
+torchvision

brainscore_vision/models/efficientnet_b0/test.py

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+import pytest
+import brainscore_vision
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('efficientnet_b0')
+    assert model.identifier == 'efficientnet_b0'

brainscore_vision/models/resnet50_VITO_8deg_cc/__init__.py

@@ -0,0 +1,9 @@
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from brainscore_vision import model_registry
+from .model import get_layers, get_model
+
+
+model_registry['resnet50-VITO-8deg-cc'] = lambda: ModelCommitment(identifier='resnet50-VITO-8deg-cc',
+                                                                    activations_model=get_model('resnet50-VITO-8deg-cc'),
+                                                                    layers=get_layers('resnet50-VITO-8deg-cc'))
+