add mobilenet_v2_1_0_128

brainscore_vision/models/mobilenet_v2_1_0_128/__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['mobilenet_v2_1_0_128'] = \
+    lambda: ModelCommitment(identifier='mobilenet_v2_1_0_128', activations_model=get_model('mobilenet_v2_1_0_128'), layers=get_layers('mobilenet_v2_1_0_128'))

brainscore_vision/models/mobilenet_v2_1_0_128/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
+from brainscore_vision.model_helpers.s3 import load_weight_file
+import torch
+import imp
+
+model_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
+                                    relative_path="mobilenet_v2_1.0_128/mobilenet_v2_1.py",
+                                    version_id="null",
+                                    sha1="c5f5fe31e92fc8fee3580f1703fcd3db74f6a753")
+model_weight_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
+                                    relative_path="mobilenet_v2_1.0_128/mobilenet_v2_1.0_128_frozen.pth",
+                                    version_id="null",
+                                    sha1="5f7f2f0117fc74d16e839bddf63036e6ef6b3e42")
+MainModel = imp.load_source('MainModel',model_path.as_posix())
+model = torch.load(model_weight_path.as_posix())
+
+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 == 'mobilenet_v2_1_0_128'
+    preprocessing = functools.partial(load_preprocess_images, image_size=128, preprocess_type='inception')
+    wrapper = PytorchWrapper(identifier=name, model=model, preprocessing=preprocessing)
+    wrapper.image_size = 128
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'mobilenet_v2_1_0_128'
+    layer_names = (['MobilenetV2_Conv_Conv2D'] +
+                   [f'MobilenetV2_expanded_conv_{i}_expand_Conv2D' for i in range(1, 17)] +
+                   ['MobilenetV2_Conv_1_Conv2D'])
+    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/abs-1801-04381,
+  author       = {Mark Sandler and
+                  Andrew G. Howard and
+                  Menglong Zhu and
+                  Andrey Zhmoginov and
+                  Liang{-}Chieh Chen},
+  title        = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
+                  Detection and Segmentation},
+  journal      = {CoRR},
+  volume       = {abs/1801.04381},
+  year         = {2018},
+  url          = {http://arxiv.org/abs/1801.04381},
+  eprinttype    = {arXiv},
+  eprint       = {1801.04381},
+  timestamp    = {Tue, 12 Jan 2021 15:30:06 +0100},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1801-04381.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/mobilenet_v2_1_0_128/requirements.txt

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+imp
+torch

brainscore_vision/models/mobilenet_v2_1_0_128/test.py

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+import brainscore_vision
+import pytest
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('mobilenet_v2_1_0_128')
+    assert model.identifier == 'mobilenet_v2_1_0_128'