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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 | ||
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model_registry['resnet50_11ad3fa6'] = lambda: ModelCommitment( | ||
identifier='resnet50_11ad3fa6', | ||
activations_model=get_model('resnet50_11ad3fa6'), | ||
layers=get_layers('resnet50_11ad3fa6') | ||
) |
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from brainscore_vision.model_helpers.check_submission import check_models | ||
import functools | ||
import torchvision.models | ||
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper | ||
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images | ||
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# This is an example implementation for submitting resnet-50 as a pytorch model | ||
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# 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. | ||
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def get_model(name): | ||
assert name == 'resnet50_11ad3fa6' | ||
model = torchvision.models.resnet50(pretrained=True) | ||
preprocessing = functools.partial(load_preprocess_images, image_size=224) | ||
wrapper = PytorchWrapper( | ||
identifier='resnet50_11ad3fa6', model=model, preprocessing=preprocessing) | ||
wrapper.image_size = 224 | ||
return wrapper | ||
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def get_layers(name): | ||
assert name == 'resnet50_11ad3fa6' | ||
return ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc'] | ||
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def get_bibtex(model_identifier): | ||
return """""" | ||
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if __name__ == '__main__': | ||
check_models.check_base_models(__name__) |
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torchvision | ||
torch |
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import pytest | ||
import brainscore_vision | ||
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@pytest.mark.travis_slow | ||
def test_has_identifier(): | ||
model = brainscore_vision.load_model('resnet50_tutorial') | ||
assert model.identifier == 'resnet50_tutorial' |
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