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from numpy.linalg import norm
from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning
import torch
from PIL import Image
url1='http://farm3.staticflickr.com/2519/4126738647_cc436c111b_z.jpg'
cap1='A motorcycle sits parked across from a herd of livestock'
url2='http://farm3.staticflickr.com/2046/2003879022_1b4b466d1d_z.jpg'
cap2='Motorcycle on platform to be worked on in garage'
url3='https://i.natgeofe.com/n/548467d8-c5f1-4551-9f58-6817a8d2c45e/NationalGeographic_2572187_3x2.jpg'
cap3='a cat laying down stretched out near a laptop'
img1 = {
'flickr_url': url1,
'caption': cap1,
'image_path' : './shared_data/motorcycle_1.jpg',
'tensor_path' : './shared_data/motorcycle_1'
}
img2 = {
'flickr_url': url2,
'caption': cap2,
'image_path' : './shared_data/motorcycle_2.jpg',
'tensor_path' : './shared_data/motorcycle_2'
}
img3 = {
'flickr_url' : url3,
'caption': cap3,
'image_path' : './shared_data/cat_1.jpg',
'tensor_path' : './shared_data/cat_1'
}
def bt_embeddings_from_local(text, image):
model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
processed_inputs = processor(image, text, padding=True, return_tensors="pt")
outputs = model(**processed_inputs)
cross_modal_embeddings = outputs.cross_embeds
text_embeddings = outputs.text_embeds
image_embeddings = outputs.image_embeds
return {
'cross_modal_embeddings': cross_modal_embeddings,
'text_embeddings': text_embeddings,
'image_embeddings': image_embeddings
}
def save_embeddings():
for img in [img1, img2, img3]:
embedding = bt_embeddings_from_local(img['caption'], Image.open(img['image_path']))
print(embedding['cross_modal_embeddings'][0].shape) #<class 'torch.Tensor'>
torch.save(embedding['cross_modal_embeddings'][0], img['tensor_path'] + '.pt')
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