brain-score.org submission (user:645) | (public:True)

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

brainscore_vision/models/fabianResNet/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+from torchvision.models import resnet18
+from torch import nn
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
+import numpy as np
+from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
+
+
+class FabianResNet(torch.nn.Module):
+    def __init__(self):
+        super(FabianResNet, self).__init__()
+        self.model = models.resnet18(weights=None)
+        self.model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.model.maxpool = nn.Identity()  # Remove maxpool
+        self.model.fc = nn.Sequential(
+            nn.Dropout(0.5),  # Dropout to avoid overfitting
+            nn.Linear(self.model.fc.in_features, 200)  # Tiny ImageNet has 200 classes
+        )
+
+    def forward(self, x):
+        activations = {}
+
+        x = self.model.conv1(x)
+        x = self.model.bn1(x)
+        x = self.model.relu(x)
+        activations["conv1"] = x
+
+        x = self.model.layer1(x)
+        activations["layer1"] = x
+        x = self.model.layer2(x)
+        activations["layer2"] = x
+        x = self.model.layer3(x)
+        activations["layer3"] = x
+        x = self.model.layer4(x)
+        activations["layer4"] = x
+
+        x = self.model.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.model.fc(x)
+        activations["fc"] = x
+
+        return activations
+
+def get_model_list():
+    return ['fabianResNet']
+
+
+def get_model(name):
+    assert name == 'fabianResNet'
+    preprocessing = functools.partial(load_preprocess_images, image_size=64)
+    activations_model = PytorchWrapper(identifier='fabianResNet', model=FabianResNet(), preprocessing=preprocessing)
+    model = ModelCommitment(identifier='fabianResNet', activations_model=activations_model,
+                            layers=['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc'])
+    model.image_size = 64
+    return model
+
+
+def get_layers(name):
+    assert name == 'fabianResNet'
+    return ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']
+
+
+def get_bibtex(model_identifier):
+    return """FabianResNetModel"""
+
+
+if __name__ == '__main__':
+    check_models.check_base_models(__name__)

brainscore_vision/models/fabianResNet/requirements.txt

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+torch
+torchvision
+Pillow
+matplotlib
+numpy
+datasets
+tensorboard

brainscore_vision/models/fabianResNet/test.py

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+import pytest
+import brainscore_vision
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('fabianResNet')
+    assert model.identifier == 'fabianResNet'