add vgg16_less_variation_1 to models

brainscore_vision/models/vgg16_less_variation_1/__init__.py

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+
+from brainscore_vision import model_registry
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+model_registry['vgg16_less_variation_iteration=1'] = lambda: ModelCommitment(identifier='vgg16_less_variation_iteration=1')

brainscore_vision/models/vgg16_less_variation_1/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+import numpy as np
+import torch
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from PIL import Image
+import open_clip
+from torch import nn
+import pytorch_lightning as pl
+import torchvision.models as models
+import gdown
+import glob
+import os
+
+device = "cpu"
+keyword = 'less_variation'
+iteration = 1
+network = 'vgg16'
+url = f"https://eggerbernhard.ch/shreya/vgg16_less_variation_iteration=1.ckpt"
+output = f"vgg16_less_variation_iteration=1.ckpt"
+gdown.download(url, output)
+
+if keyword != 'imagenet_trained' and keyword != 'no_training':
+    lx_whole = list(glob.glob(f"vgg16_less_variation_iteration=1.ckpt"))
+    print(lx_whole)
+    if len(lx_whole) == 0:
+        continue
+    if len(lx_whole) > 1:
+        lx_whole = [lx_whole[-1]]
+elif keyword == 'imagenet_trained' or keyword == 'no_training':
+    print('keyword is imagenet')
+    lx_whole = ['x']
+
+for model_ckpt in lx_whole:
+    print(model_ckpt)
+    last_module_name = None
+    last_module = None
+    layers = []
+    if keyword == 'imagenet_trained' and network != 'clip':
+        model = torch.hub.load('pytorch/vision', network, pretrained=True)
+        for name, module in model.named_modules():
+            last_module_name = name
+            last_module = module
+            layers.append(name)
+    else:
+        model = torch.hub.load('pytorch/vision', network, pretrained=False)
+    if model_ckpt != 'x':
+        ckpt = torch.load(model_ckpt, map_location='cpu')
+    if model_ckpt != 'x' and network == 'alexnet' and keyword != 'imagenet_trained':
+        ckpt2 = {}
+        for keys in ckpt['state_dict']:
+            print(keys)
+            print(ckpt['state_dict'][keys].shape)
+            print('---')
+            k2 = keys.split('model.')[1]
+            ckpt2[k2] = ckpt['state_dict'][keys]
+        model.load_state_dict(ckpt2)
+    if model_ckpt != 'x' and network == 'vgg16' and keyword != 'imagenet_trained':
+        ckpt2 = {}
+        for keys in ckpt['state_dict']:
+            print(keys)
+            print(ckpt['state_dict'][keys].shape)
+            print('---')
+            k2 = keys.split('model.')[1]
+            ckpt2[k2] = ckpt['state_dict'][keys]
+        model.load_state_dict(ckpt2)
+    # Add more cases for other networks as needed
+
+def get_bibtex(model_identifier):
+    return "VGG16"
+
+def get_model_list():
+    return [f'vgg16_less_variation_iteration=1']
+
+def get_model(name):
+    assert name == f'vgg16_less_variation_iteration=1'
+    url = f"https://eggerbernhard.ch/shreya/vgg16_less_variation_iteration=1.ckpt"
+    output = f"vgg16_less_variation_iteration=1.ckpt"
+    gdown.download(url, output)
+
+    preprocessing = functools.partial(load_preprocess_images, image_size=224)
+    activations_model = PytorchWrapper(identifier=name, model=model, preprocessing=preprocessing)
+
+    return activations_model
+
+def get_layers(name):
+    assert name == f'vgg16_less_variation_iteration=1.ckpt'
+    layers = []
+    url = f"https://eggerbernhard.ch/shreya/vgg16_less_variation_iteration=1.ckpt"
+    output = f"https://eggerbernhard.ch/shreya/vgg16_less_variation_iteration=1.ckpt"
+    gdown.download(url, output)
+    for name, module in model.named_modules():
+        layers.append(name)
+    return layers
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/vgg16_less_variation_1/setup.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from setuptools import setup, find_packages
+
+requirements = [
+    "torchvision",
+    "torch",
+    "gdown",
+    "pytorch_lightning",
+    "open_clip",
+]
+
+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/vgg16_less_variation_1/test.py

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+import pytest