diff --git a/brainscore_vision/models/alexnet_4_all/__init__.py b/brainscore_vision/models/alexnet_4_all/__init__.py new file mode 100644 index 000000000..b22f0e0ea --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/__init__.py @@ -0,0 +1,10 @@ + +from brainscore_vision import model_registry +from brainscore_vision.model_helpers.brain_transformation import ModelCommitment +from .model import get_model, MODEL_CONFIGS, get_layers +network = 'alexnet' +for keyword in ['imagenet_trained', 'less_variation', 'no_variation', 'z_axis', 'wo_shading','wo_shadows', 'ambient', 'textures', 'no_specular']: + for iteration in range(1,6): + model_registry[f'{network}_{keyword}_iteration={iteration}'] = lambda: ModelCommitment(identifier=f'{network}_{keyword}_iteration={iteration}', activations_model=get_model('{network}_{keyword}_iteration={iteration}'), layers=get_layers(f'{network}_{keyword}_iteration={iteration}')) + + diff --git a/brainscore_vision/models/alexnet_4_all/config.json b/brainscore_vision/models/alexnet_4_all/config.json new file mode 100644 index 000000000..22c93eb91 --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/config.json @@ -0,0 +1,1352 @@ +{ + "alexnet_imagenet_trained_iteration=1" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_imagenet_trained_iteration=2" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_imagenet_trained_iteration=3" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_imagenet_trained_iteration=4" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_imagenet_trained_iteration=5" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_less_variation_iteration=1" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_less_variation_iteration=2" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_less_variation_iteration=3" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_less_variation_iteration=4" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_less_variation_iteration=5" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_variation_iteration=1" : { + "model_name": "alexnet", + "keyword": "no_variation", + "model_id": "alexnet_no_variation_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_variation_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_variation_iteration=2" : { + "model_name": "alexnet", + "keyword": "no_variation", + "model_id": "alexnet_no_variation_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_variation_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", 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"textures", + "model_id": "alexnet_textures_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_textures_iteration=2" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_textures_iteration=3" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", 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"alexnet_textures_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", 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"layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_specular_iteration=1" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_specular_iteration=2" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_specular_iteration=3" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_specular_iteration=4" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + }, + + "alexnet_no_specular_iteration=5" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } + } +} + \ No newline at end of file diff --git a/brainscore_vision/models/alexnet_4_all/config.txt b/brainscore_vision/models/alexnet_4_all/config.txt new file mode 100644 index 000000000..878182106 --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/config.txt @@ -0,0 +1,1351 @@ +{ +"alexnet_imagenet_trained_iteration=1" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", 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"alexnet_imagenet_trained_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + 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"layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_imagenet_trained_iteration=4" : { + "model_name": "alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + 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"alexnet", + "keyword": "imagenet_trained", + "model_id": "alexnet_imagenet_trained_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/imagenet_trained_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_less_variation_iteration=1" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + 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+"alexnet_less_variation_iteration=3" : { + "model_name": "alexnet", + "keyword": "less_variation", + "model_id": "alexnet_less_variation_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/less_variation_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", 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+ "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", 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+ "keyword": "ambient", + "model_id": "alexnet_ambient_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/ambient_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_textures_iteration=1" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_textures_iteration=2" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_textures_iteration=3" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", 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"layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_textures_iteration=5" : { + "model_name": "alexnet", + "keyword": "textures", + "model_id": "alexnet_textures_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/textures_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_no_specular_iteration=1" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=1", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_1.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_no_specular_iteration=2" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=2", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_2.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_no_specular_iteration=3" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=3", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_3.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_no_specular_iteration=4" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=4", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_4.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +}, + +"alexnet_no_specular_iteration=5" : { + "model_name": "alexnet", + "keyword": "no_specular", + "model_id": "alexnet_no_specular_iteration=5", + "resize_size": 256, + "crop_size": 224, + "checkpoint_url": "https://eggerbernhard.ch/latest_alexnet/no_specular_5.ckpt", + "model_commitment": { + "layers": [ + "conv1", "bn1", "relu", "maxpool", + "layer1", "layer1.0", "layer1.0.conv1", + "layer1.0.bn1", "layer1.0.relu", "layer1.0.conv2", + "layer1.0.bn2", "layer1.0.relu_2", "layer1.0.conv3", + "layer1.0.bn3", "layer1.0.shortcut", + "layer1.0.shortcut.conv1", "layer1.0.shortcut.bn1", + "layer2", "layer2.0", "layer2.0.conv1", "layer2.0.bn1", + "layer2.0.relu", "layer2.0.conv2", "layer2.0.bn2", + "layer2.0.relu_2", "layer2.0.conv3", "layer2.0.bn3", + "layer2.0.shortcut", "layer2.0.shortcut.conv1", "layer2.0.shortcut.bn1", + "layer3", "layer3.0", "layer3.0.conv1", "layer3.0.bn1", "layer3.0.relu", + "layer3.0.conv2", "layer3.0.bn2", "layer3.0.relu_2", "layer3.0.conv3", "layer3.0.bn3", + "layer3.0.shortcut", "layer3.0.shortcut.conv1", "layer3.0.shortcut.bn1", "layer4", "layer4.0", + "layer4.0.conv1", "layer4.0.bn1", "layer4.0.relu", "layer4.0.conv2", "layer4.0.bn2", "layer4.0.relu_2", + "layer4.0.conv3", "layer4.0.bn3", "layer4.0.shortcut", "layer4.0.shortcut.conv1", "layer4.0.shortcut.bn1", "fc" + + + ] + } +} +} diff --git a/brainscore_vision/models/alexnet_4_all/model.py b/brainscore_vision/models/alexnet_4_all/model.py new file mode 100644 index 000000000..9d986987e --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/model.py @@ -0,0 +1,177 @@ + +import os +import functools +import json +from pathlib import Path +import ssl + +import torchvision.models +import torch +import gdown + +from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper +from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + +import timm +import numpy as np +import torchvision.transforms as T +from PIL import Image + +import albumentations as A +from albumentations.pytorch import ToTensorV2 + +# Disable SSL verification +ssl._create_default_https_context = ssl._create_unverified_context + +BIBTEX="""""" + +with open(Path(__file__).parent / "model_configs.json", "r") as f: + MODEL_CONFIGS = json.load(f) + + +def load_image(image_filepath): + return Image.open(image_filepath).convert("RGB") + + +def get_interpolation_mode(interpolation: str) -> int: + #Returns the interpolation mode for albumentations + if "linear" or "bilinear" in interpolation: + return 1 + elif "cubic" or "bicubic" in interpolation: + return 2 + else: + raise NotImplementedError(f"Interpolation mode {interpolation} not implemented") + + +def custom_image_preprocess( + images, + resize_size: int, + crop_size: int, + interpolation: str, + transforms=None, +): + if transforms is None: + interpolation = get_interpolation_mode(interpolation) + transforms = A.Compose( + [ + A.Resize(resize_size, resize_size, p=1.0, interpolation=interpolation), + A.CenterCrop(crop_size, crop_size, p=1.0), + A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ToTensorV2(), + ] + ) + if isinstance(transforms, T.Compose): + images = [transforms(image) for image in images] + images = [np.array(image) for image in images] + images = np.stack(images) + elif isinstance(transforms, A.Compose): + images = [transforms(image=np.array(image))["image"] for image in images] + images = np.stack(images) + else: + raise NotImplementedError( + f"Transform of type {type(transforms)} is not implemented" + ) + + return images + + +def load_preprocess_images_custom( + image_filepaths, preprocess_images=custom_image_preprocess, **kwargs +): + images = [load_image(image_filepath) for image_filepath in image_filepaths] + images = preprocess_images(images, **kwargs) + return images + + +def get_model(model_id:str): + + # Unpack model config + config = MODEL_CONFIGS[model_id] + model_name = config["model_name"] + model_id = config["model_id"] + resize_size = config["resize_size"] + crop_size = config["crop_size"] + interpolation = config["interpolation"] + num_classes = config["num_classes"] + ckpt_url = config["checkpoint_url"] + use_timm = config["use_timm"] + timm_model_name = config["timm_model_name"] + epoch = config["epoch"] + load_model_ema = config["load_model_ema"] + output_head = config["output_head"] + is_vit = config["is_vit"] + keyword = config["keyword"] + network = config["network"] + + # Temporary fix for vit models + # See https://github.com/brain-score/vision/pull/1232 + if is_vit: + os.environ['RESULTCACHING_DISABLE'] = 'brainscore_vision.model_helpers.activations.core.ActivationsExtractorHelper._from_paths_stored' + + device = "cpu" + url = config["checkpoint_url"] + config = MODEL_CONFIGS[model_id] + model_name = config["model_name"] + resize_size = config["resize_size"] + crop_size = config["crop_size"] + output = "checkpoint.ckpt" + gdown.download(url, output) + + + + if keyword != 'imagenet_trained' and keyword != 'no_training': + lx_whole = [f"checkpoint.ckpt"] + 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) + + + + # Wrap model + preprocessing = functools.partial( + load_preprocess_images_custom, + resize_size=resize_size, + crop_size=crop_size, + interpolation=interpolation, + transforms=None + ) + wrapper = PytorchWrapper( + identifier=model_id, model=model, preprocessing=preprocessing + ) + return wrapper diff --git a/brainscore_vision/models/alexnet_4_all/setup.py b/brainscore_vision/models/alexnet_4_all/setup.py new file mode 100644 index 000000000..64f80c7d6 --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/setup.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from setuptools import setup, find_packages + +requirements = [ + "torchvision", + "torch", + "gdown", + "pytorch_lightning", + "brainscore_vision" +] + +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', +) diff --git a/brainscore_vision/models/alexnet_4_all/test.py b/brainscore_vision/models/alexnet_4_all/test.py new file mode 100644 index 000000000..d03a9a5bd --- /dev/null +++ b/brainscore_vision/models/alexnet_4_all/test.py @@ -0,0 +1,3 @@ + +import pytest +