add yudixie_resnet18_240719_11 to models

brainscore_vision/models/yudixie_resnet18_240719_11/__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
+
+
+def commit_model(identifier):
+    return ModelCommitment(identifier=identifier,
+                           activations_model=get_model(identifier),
+                           layers=get_layers(identifier))
+
+model_registry['yudixie_resnet18_random_0_240719'] = lambda: commit_model('yudixie_resnet18_random_0_240719')

brainscore_vision/models/yudixie_resnet18_240719_11/model.py

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+import os
+from pathlib import Path
+import functools
+from urllib.request import urlretrieve
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torchvision.models import resnet18
+
+from brainscore_vision.model_helpers.check_submission import check_models
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper, load_preprocess_images
+
+
+# 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.
+
+
+def get_model(name):
+    pytorch_device = torch.device('cpu')
+
+    weigth_url = f'https://yudi-brainscore-models.s3.amazonaws.com/{name}.pth'
+    fh = urlretrieve(weigth_url, f'{name}_weights.pth')
+    load_path = fh[0]
+
+    pytorch_model = resnet18()
+    pytorch_model.fc = nn.Linear(pytorch_model.fc.in_features, 674)
+    pytorch_model = pytorch_model.to(pytorch_device)
+
+    # load model from saved weights
+    saved_state_dict = torch.load(load_path, map_location=pytorch_device)
+    state_dict = {}
+    for k, v in saved_state_dict.items():
+        if k.startswith('_orig_mod.'):
+            # for compiled models
+            state_dict[k[10:]] = v
+        else:
+            state_dict[k] = v
+    pytorch_model.load_state_dict(state_dict, strict=True)
+    print(f'Loaded model from {load_path}')
+
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    wrapper = PytorchWrapper(identifier=name,
+                             model=pytorch_model,
+                             preprocessing=preprocessing)
+    wrapper.image_size = 224
+    return wrapper
+
+
+def get_layers(name):
+    return ['conv1','layer1', 'layer2', 'layer3', 'layer4', 'fc']
+
+
+def get_bibtex(model_identifier):
+    return """xx"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/yudixie_resnet18_240719_11/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/yudixie_resnet18_240719_11/test.py

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+# Left empty as part of 2023 models migration