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add eMMCR_Mom_lmda_02_1 to models (#781)
Co-authored-by: AutoJenkins <[email protected]>
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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["eMMCR_Mom_lmda_02_1"] = lambda: ModelCommitment( | ||
identifier="eMMCR_Mom_lmda_02_1", | ||
activations_model=get_model("eMMCR_Mom_lmda_02_1"), | ||
layers=get_layers("eMMCR_Mom_lmda_02_1"), | ||
) |
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from brainscore_vision.model_helpers.check_submission import check_models | ||
import functools | ||
import os | ||
from urllib.request import urlretrieve | ||
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 | ||
from collections import OrderedDict | ||
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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. | ||
from brainscore_vision.model_helpers.check_submission import check_models | ||
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def get_model_list(): | ||
return ["eMMCR_Mom_lmda_02_1"] | ||
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def get_model(name): | ||
assert name == "eMMCR_Mom_lmda_02_1" | ||
url = "https://users.flatironinstitute.org/~tyerxa/equi_proj/training_checkpoints/fresh/paired/lmda_0.2/MMCR_Momentum_1/latest-rank0" | ||
fh = urlretrieve(url) | ||
state_dict = torch.load(fh[0], map_location=torch.device("cpu"))["state"]["model"] | ||
model = load_composer_classifier(state_dict) | ||
preprocessing = functools.partial(load_preprocess_images, image_size=224) | ||
wrapper = PytorchWrapper(identifier=name, model=model, preprocessing=preprocessing) | ||
wrapper.image_size = 224 | ||
return wrapper | ||
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def load_composer_classifier(sd): | ||
model = torchvision.models.resnet.resnet50() | ||
new_sd = OrderedDict() | ||
for k, v in sd.items(): | ||
if 'lin_cls' in k: | ||
new_sd['fc.' + k.split('.')[-1]] = v | ||
if ".f." not in k: | ||
continue | ||
parts = k.split(".") | ||
idx = parts.index("f") | ||
new_k = ".".join(parts[idx + 1 :]) | ||
new_sd[new_k] = v | ||
model.load_state_dict(new_sd, strict=True) | ||
return model | ||
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def get_layers(name): | ||
assert name == "eMMCR_Mom_lmda_02_1" | ||
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outs = ["conv1", "layer1", "layer2", "layer3", "layer4", "avgpool", "fc"] | ||
outs = ["layer4"] | ||
return outs | ||
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def get_bibtex(model_identifier): | ||
return """xx""" | ||
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if __name__ == "__main__": | ||
check_models.check_base_models(__name__) |
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#!/usr/bin/env python | ||
# -*- coding: utf-8 -*- | ||
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from setuptools import setup, find_packages | ||
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requirements = [ "torchvision", | ||
"torch" | ||
] | ||
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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', | ||
) |
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# Left empty as part of 2023 models migration |