add mobilenet_v2_0_5_192

brainscore_vision/models/mobilenet_v2_0_5_192/__init__.py

@@ -0,0 +1,7 @@
+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_0_5_192'] = \
+    lambda: ModelCommitment(identifier='mobilenet_v2_0_5_192', activations_model=get_model('mobilenet_v2_0_5_192'), layers=get_layers('mobilenet_v2_0_5_192'))

brainscore_vision/models/mobilenet_v2_0_5_192/model.py

@@ -0,0 +1,73 @@
+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_0.5_192/mobilenet_v2_0.py",
+                                    version_id="null",
+                                    sha1="d5c7af8768f9f2475367ac1e48e204cc5cf004a0")
+model_weight_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
+                                    relative_path="mobilenet_v2_0.5_192/mobilenet_v2_0.5_192_frozen.pth",
+                                    version_id="null",
+                                    sha1="e5aa083caa4833fccd48af0c578a45064824dd7f")
+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_0_5_192'
+    preprocessing = functools.partial(load_preprocess_images, image_size=192, preprocess_type='inception')
+    wrapper = PytorchWrapper(identifier=name, model=model, preprocessing=preprocessing)
+    wrapper.image_size = 192
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'mobilenet_v2_0_5_192'
+    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_0_5_192/region_layer_map/mobilenet_v2_0_5_192.json

@@ -0,0 +1,6 @@
+{
+    "V1": "MobilenetV2_expanded_conv_9_expand_Conv2D",
+    "V2": "MobilenetV2_expanded_conv_8_expand_Conv2D",
+    "V4": "MobilenetV2_expanded_conv_7_expand_Conv2D",
+    "IT": "MobilenetV2_expanded_conv_14_expand_Conv2D"
+}

brainscore_vision/models/mobilenet_v2_0_5_192/requirements.txt

@@ -0,0 +1,2 @@
+imp
+torch

brainscore_vision/models/mobilenet_v2_0_5_192/test.py

@@ -0,0 +1,8 @@
+import brainscore_vision
+import pytest
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('mobilenet_v2_0_5_192')
+    assert model.identifier == 'mobilenet_v2_0_5_192'