Add inception_v1

brainscore_vision/models/inception_v1/__init__.py

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+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from brainscore_vision import model_registry
+from .model import get_layers,get_model
+
+
+model_registry['inception_v1'] = \
+    lambda: ModelCommitment(identifier='inception_v1', activations_model=get_model('inception_v1'), layers=get_layers('inception_v1'))

brainscore_vision/models/inception_v1/model.py

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+import functools
+import torch
+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
+
+
+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 == 'inception_v1'
+    preprocessing = functools.partial(load_preprocess_images, image_size=224, preprocess_type='inception')
+    model = torch.hub.load('pytorch/vision:v0.10.0', 'googlenet', pretrained=True)
+    wrapper = PytorchWrapper(identifier='inception_v1', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'inception_v1'
+    layer_names = (['maxpool2'] +
+                   [f'inception3{i}' for i in ['a', 'b']] +
+                   [f'inception4{i}' for i in ['a', 'b', 'c', 'd', 'e']] +
+                   [f'inception5{i}' for i in ['a', 'b']] +
+                   ['avgpool'])
+    return layer_names
+
+
+def get_bibtex(name):
+    """
+    A method returning the bibtex reference of the requested model as a string.
+    """
+    return '''
+@article{DBLP:journals/corr/SzegedyLJSRAEVR14,
+  author       = {Christian Szegedy and
+                  Wei Liu and
+                  Yangqing Jia and
+                  Pierre Sermanet and
+                  Scott E. Reed and
+                  Dragomir Anguelov and
+                  Dumitru Erhan and
+                  Vincent Vanhoucke and
+                  Andrew Rabinovich},
+  title        = {Going Deeper with Convolutions},
+  journal      = {CoRR},
+  volume       = {abs/1409.4842},
+  year         = {2014},
+  url          = {http://arxiv.org/abs/1409.4842},
+  eprinttype    = {arXiv},
+  eprint       = {1409.4842},
+  timestamp    = {Mon, 13 Aug 2018 16:48:52 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/SzegedyLJSRAEVR14.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/inception_v1/requirements.txt

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+torch

brainscore_vision/models/inception_v1/test.py

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+import brainscore_vision
+import pytest
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('inception_v1')
+    assert model.identifier == 'inception_v1'