Add resnet50_tutorial to model registry (#1014)

* Add resnet50_tutorial to model registry

* resnet50_tutorial now inline with 2.0 model packaging

* remove setup.py and add requirements.txt

* add standard test and make sure model identifiers line up

* Update brainscore_vision/models/resnet50_tutorial/model.py

Co-authored-by: Martin Schrimpf <[email protected]>

---------

Co-authored-by: Martin Schrimpf <[email protected]>

brainscore_vision/models/resnet50_tutorial/__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_tutorial'] = lambda: ModelCommitment(identifier='resnet50_tutorial', activations_model=get_model('resnet50_tutorial'), layers=get_layers('resnet50_tutorial'))

brainscore_vision/models/resnet50_tutorial/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+import torchvision.models
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+
+# This is an example implementation for submitting resnet-50 as a pytorch model
+
+# Attention: It is important, that the wrapper identifier is unique per model!
+# The results will otherwise be the same due to brain-scores internal result caching mechanism.
+# Please load your pytorch model for usage in CPU. There won't be GPUs available for scoring your model.
+# If the model requires a GPU, contact the brain-score team directly.
+
+
+def get_model(name):
+    assert name == 'resnet50_tutorial'
+    model = torchvision.models.resnet50(pretrained=True)
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    wrapper = PytorchWrapper(identifier='resnet50_tutorial', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'resnet50_tutorial'
+    return ['conv1','layer1', 'layer2', 'layer3', 'layer4', 'fc']
+
+
+def get_bibtex(model_identifier):
+    return """"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/resnet50_tutorial/requirements.txt

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+torchvision
+torch

brainscore_vision/models/resnet50_tutorial/test.py

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+import pytest
+import brainscore_vision
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('resnet50_tutorial')
+    assert model.identifier == 'resnet50_tutorial'