add cvt_cvt_13_224_in1k_4 to models

brainscore_vision/models/cvt_cvt_13_224_in1k_4/__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['cvt_cvt-13-224-in1k_4'] = \
+    lambda: ModelCommitment(identifier='cvt_cvt-13-224-in1k_4',
+                            activations_model=get_model('cvt_cvt-13-224-in1k_4'),
+                            layers=get_layers('cvt_cvt-13-224-in1k_4'))

brainscore_vision/models/cvt_cvt_13_224_in1k_4/model.py

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+from brainscore_vision.model_helpers.check_submission import check_models
+import functools
+from transformers import AutoFeatureExtractor, CvtForImageClassification
+from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
+from PIL import Image
+import numpy as np
+import torch
+
+"""
+Template module for a base model submission to brain-score
+"""
+
+
+def get_model(name):
+    assert name == 'cvt_cvt-13-224-in1k_4'
+    # https://huggingface.co/models?sort=downloads&search=cvt
+    image_size = 224
+    processor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-13')
+    model = CvtForImageClassification.from_pretrained('microsoft/cvt-13')
+    preprocessing = functools.partial(load_preprocess_images, processor=processor, image_size=image_size)
+    wrapper = PytorchWrapper(identifier=name, model=model, preprocessing=preprocessing)
+    wrapper.image_size = image_size
+
+    return wrapper
+
+
+def get_layers(name):
+    assert name == 'cvt_cvt-13-224-in1k_4'
+    layers = []
+    layers += [f'cvt.encoder.stages.0.layers.{i}' for i in range(1)]
+    layers += [f'cvt.encoder.stages.1.layers.{i}' for i in range(2)]
+    layers += [f'cvt.encoder.stages.2.layers.{i}' for i in range(10)]
+    layers += ['layernorm']
+    return layers
+
+
+def get_bibtex(model_identifier):
+    """
+    A method returning the bibtex reference of the requested model as a string.
+    """
+    return ''
+
+
+def load_preprocess_images(image_filepaths, image_size, processor=None, **kwargs):
+    images = load_images(image_filepaths)
+    # images = [<PIL.Image.Image image mode=RGB size=400x400 at 0x7F8654B2AC10>, ...]
+    images = [image.resize((image_size, image_size)) for image in images]
+    if processor is not None:
+        images = [processor(images=image, return_tensors="pt", **kwargs) for image in images]
+        if len(images[0].keys()) != 1:
+            raise NotImplementedError(f'unknown processor for getting model {processor}')
+        assert list(images[0].keys())[0] == 'pixel_values'
+        images = [image['pixel_values'] for image in images]
+        images = torch.cat(images)
+        images = images.cpu().numpy()
+    else:
+        images = preprocess_images(images, image_size=image_size, **kwargs)
+    return images
+
+
+def load_images(image_filepaths):
+    return [load_image(image_filepath) for image_filepath in image_filepaths]
+
+
+def load_image(image_filepath):
+    with Image.open(image_filepath) as pil_image:
+        if 'L' not in pil_image.mode.upper() and 'A' not in pil_image.mode.upper() \
+                and 'P' not in pil_image.mode.upper():  # not binary and not alpha and not palletized
+            # work around to https://github.com/python-pillow/Pillow/issues/1144,
+            # see https://stackoverflow.com/a/30376272/2225200
+            return pil_image.copy()
+        else:  # make sure potential binary images are in RGB
+            rgb_image = Image.new("RGB", pil_image.size)
+            rgb_image.paste(pil_image)
+            return rgb_image
+
+
+def preprocess_images(images, image_size, **kwargs):
+    preprocess = torchvision_preprocess_input(image_size, **kwargs)
+    images = [preprocess(image) for image in images]
+    images = np.concatenate(images)
+    return images
+
+
+def torchvision_preprocess_input(image_size, **kwargs):
+    from torchvision import transforms
+    return transforms.Compose([
+        transforms.Resize((image_size, image_size)),
+        torchvision_preprocess(**kwargs),
+    ])
+
+
+def torchvision_preprocess(normalize_mean=(0.485, 0.456, 0.406), normalize_std=(0.229, 0.224, 0.225)):
+    from torchvision import transforms
+    return transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=normalize_mean, std=normalize_std),
+        lambda img: img.unsqueeze(0)
+    ])
+
+
+def create_static_video(image, num_frames, normalize_0to1=False, channel_dim=3):
+    '''
+    Create a static video with the same image in all frames.
+    Args:
+        image (PIL.Image.Image): Input image.
+        num_frames (int): Number of frames in the video.
+    Returns:
+        result (np.ndarray): np array of frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    for _ in range(num_frames):
+        frame = np.array(image)
+        if normalize_0to1:
+            frame = frame / 255.
+        if channel_dim == 1:
+            frame = frame.transpose(2, 0, 1)
+        frames.append(frame)
+    return np.stack(frames)
+
+
+if __name__ == '__main__':
+    # Use this method to ensure the correctness of the BaseModel implementations.
+    # It executes a mock run of brain-score benchmarks.
+    check_models.check_base_models(__name__)
+
+"""
+Notes on the error:
+
+- 'channel_x' key error: 
+# 'embeddings.patch_embeddings.projection',
+https://github.com/search?q=repo%3Abrain-score%2Fmodel-tools%20channel_x&type=code
+
+"""

brainscore_vision/models/cvt_cvt_13_224_in1k_4/requirements.txt

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+numpy
+torch
+transformers==4.30.2
+pillow

brainscore_vision/models/cvt_cvt_13_224_in1k_4/test.py

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+import pytest
+import brainscore_vision
+
+
+@pytest.mark.travis_slow
+def test_has_identifier():
+    model = brainscore_vision.load_model('cvt_cvt-13-224-in1k_4')
+    assert model.identifier == 'cvt_cvt-13-224-in1k_4'