add dbp_resnet50_julios to models (#531)

Co-authored-by: AutoJenkins <[email protected]>
Co-authored-by: Khaled K Shehada <[email protected]>

brainscore_vision/models/dbp_resnet50_julios/__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['dbp_resnet50_julios'] = lambda: ModelCommitment(identifier='dbp_resnet50_julios', activations_model=get_model('dbp_resnet50_julios'), layers=get_layers('dbp_resnet50_julios'))

brainscore_vision/models/dbp_resnet50_julios/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+import os
+import torchvision.models
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+from pathlib import Path
+from brainscore_vision.model_helpers import download_weights
+import torch
+
+# 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.
+from brainscore_vision.model_helpers.check_submission import check_models
+
+
+def get_model_list():
+    return ['dbp_resnet50_julios']
+
+
+def get_model(name):
+    assert name == 'dbp_resnet50_julios'
+    model = torch.load('brainscore_vision/models/dbp_resnet50_julios/dbp_export.pt')
+    # download_weights(
+    # bucket='brainscore-vision', 
+    # folder_path='models/resnet50-deepdive-2',
+    # filename_version_sha=[('ckpt.pth', 'C722T4BityNPpazdXWiAeu8pGBxKIudb', '0186929df5d04451995d94cd332a3603a00594fe')],
+    # save_directory=Path(__file__).parent)
+    # ckpt_path = os.path.join(os.path.dirname(__file__), 'ckpt.pth')
+    # state_dict = torch.load(ckpt_path, map_location=torch.device('cpu'))
+    # model.load_state_dict(state_dict)
+
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    print(preprocessing)
+    wrapper = PytorchWrapper(identifier='dbp_resnet50_julios', model=model, preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'dbp_resnet50_julios'
+    return ['layer1', 'layer2', 'layer3', 'layer4']  
+
+
+def get_bibtex(model_identifier):
+    return """xx"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/dbp_resnet50_julios/setup.py

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+#!/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/dbp_resnet50_julios/test.py

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+# Left empty as part of 2023 models migration