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import numpy as np
import torch
from thingsvision import get_extractor
from thingsvision.utils.storing import save_features
from thingsvision.utils.data import ImageDataset, DataLoader
source = 'custom'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = 'clip'
model_parameters = {
'variant': 'ViT-B/32'
# This model creates 512 length vectors
}
# This model is more accurate but takes longer to run and not sure we need it for the demo
# model_name = 'OpenCLIP'
# model_parameters = {
# 'variant': 'ViT-H-14',
# 'dataset': 'laion2b_s32b_b79k'
# # This model create 1024 length vectors
# }
extractor = get_extractor(
model_name=model_name,
source=source,
device=device,
pretrained=True,
model_parameters=model_parameters,
)
root='../images_000/' # (e.g., './images/)
batch_size = 32
dataset = ImageDataset(
root=root,
out_path='../test_vectors',
backend=extractor.get_backend(), # backend framework of model
transforms=extractor.get_transformations(resize_dim=256, crop_dim=224) # set the input dimensionality to whichever values are required for your pretrained model
)
batches = DataLoader(
dataset=dataset,
batch_size=batch_size,
backend=extractor.get_backend() # backend framework of model
)
module_name = 'visual'
def get_features():
# we are creating 512 length vectors
features = extractor.extract_features(
batches=batches,
module_name=module_name,
flatten_acts=True,
output_type="ndarray", # or "tensor" (only applicable to PyTorch models of which CLIP is one!)
)
# WE ARE NOT DOING THIS append the file names to the front of the vector matrix. We turn the file names into a 40 x 1
# np array #full_data = np.hstack((np.array(dataset.file_names).reshape(-1,1), features))
# The model returns the vectors in alphbetical order for the filenames. Our other code just reads through the directory
# without a sort. Therefore this needs to be a dict so we can do lookups
# save_features(features, out_path='../test_vectors', file_format='txt') # file_format can be set to "npy", "txt", "mat", "pt", or "hdf5"
vectors = {}
for i in range(len(dataset.file_names)):
vectors[dataset.file_names[i][0:16]] = features[i]
return vectors
if __name__ == '__main__':
result = get_features()
print(str(len(result)))
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