add skynet_srouce_code to models

brainscore_vision/models/skynet_srouce_code/__init__.py

@@ -0,0 +1,5 @@
+from brainscore_vision import model_registry
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from .model import get_model, get_layers
+
+model_registry['skynet_source_code'] = lambda: ModelCommitment(identifier='skynet_source_code', activations_model=get_model('skynet_source_code'), layers=get_layers('skynet_source_code'))

brainscore_vision/models/skynet_srouce_code/model.py

@@ -0,0 +1,62 @@
+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+import torch
+import numpy as np
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+
+
+# This is an example implementation for submitting custom model named my_custom_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.
+
+
+class MyCustomModel(torch.nn.Module):
+    def __init__(self):
+        super(MyCustomModel, self).__init__()
+        self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=2, kernel_size=3)
+        self.relu1 = torch.nn.ReLU()
+        linear_input_size = np.power((224 - 3 + 2 * 0) / 1 + 1, 2) * 2
+        self.linear = torch.nn.Linear(int(linear_input_size), 1000)
+        self.relu2 = torch.nn.ReLU()  # can't get named ReLU output otherwise
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.relu1(x)
+        x = x.view(x.size(0), -1)
+        x = self.linear(x)
+        x = self.relu2(x)
+        return x
+
+
+def get_model_list():
+    return ['skynet_source_code']
+
+
+def get_model(name):
+    assert name == 'skynet_source_code'
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    activations_model = PytorchWrapper(identifier='skynet_source_code', model=MyCustomModel(), preprocessing=preprocessing)
+    model = ModelCommitment(identifier='skynet_source_code', activations_model=activations_model,
+                            # specify layers to consider
+                            layers=['conv1', 'relu1', 'relu2'])
+    wrapper = PytorchWrapper(identifier='skynet_source_code', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'skynet_source_code'
+    return ['conv1', 'relu1', 'relu2']
+
+
+def get_bibtex(model_identifier):
+    return """xx"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/skynet_srouce_code/setup.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from setuptools import setup, find_packages
+
+requirements = [ "torchvision",
+                 "torch"
+]
+
+setup(
+    packages=find_packages(exclude=['tests']),
+    include_package_data=True,
+    install_requires=requirements,
+    license="MIT license",
+    zip_safe=False,
+    keywords='brain-score template',
+    classifiers=[
+        'Development Status :: 2 - Pre-Alpha',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: MIT License',
+        'Natural Language :: English',
+        'Programming Language :: Python :: 3.7',
+    ],
+    test_suite='tests',
+)

brainscore_vision/models/skynet_srouce_code/test.py

@@ -0,0 +1 @@
+# Left empty as part of 2023 models migration