Spaces:
Sleeping
Sleeping
File size: 3,310 Bytes
95b1715 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
import torch
import clip
import os
import numpy as np
imagenet_templates = [
'a bad photo of a {}.',
# 'a photo of many {}.',
'a sculpture of a {}.',
'a photo of the hard to see {}.',
'a low resolution photo of the {}.',
'a rendering of a {}.',
'graffiti of a {}.',
'a bad photo of the {}.',
'a cropped photo of the {}.',
'a tattoo of a {}.',
'the embroidered {}.',
'a photo of a hard to see {}.',
'a bright photo of a {}.',
'a photo of a clean {}.',
'a photo of a dirty {}.',
'a dark photo of the {}.',
'a drawing of a {}.',
'a photo of my {}.',
'the plastic {}.',
'a photo of the cool {}.',
'a close-up photo of a {}.',
'a black and white photo of the {}.',
'a painting of the {}.',
'a painting of a {}.',
'a pixelated photo of the {}.',
'a sculpture of the {}.',
'a bright photo of the {}.',
'a cropped photo of a {}.',
'a plastic {}.',
'a photo of the dirty {}.',
'a jpeg corrupted photo of a {}.',
'a blurry photo of the {}.',
'a photo of the {}.',
'a good photo of the {}.',
'a rendering of the {}.',
'a {} in a video game.',
'a photo of one {}.',
'a doodle of a {}.',
'a close-up photo of the {}.',
'a photo of a {}.',
'the origami {}.',
'the {} in a video game.',
'a sketch of a {}.',
'a doodle of the {}.',
'a origami {}.',
'a low resolution photo of a {}.',
'the toy {}.',
'a rendition of the {}.',
'a photo of the clean {}.',
'a photo of a large {}.',
'a rendition of a {}.',
'a photo of a nice {}.',
'a photo of a weird {}.',
'a blurry photo of a {}.',
'a cartoon {}.',
'art of a {}.',
'a sketch of the {}.',
'a embroidered {}.',
'a pixelated photo of a {}.',
'itap of the {}.',
'a jpeg corrupted photo of the {}.',
'a good photo of a {}.',
'a plushie {}.',
'a photo of the nice {}.',
'a photo of the small {}.',
'a photo of the weird {}.',
'the cartoon {}.',
'art of the {}.',
'a drawing of the {}.',
'a photo of the large {}.',
'a black and white photo of a {}.',
'the plushie {}.',
'a dark photo of a {}.',
'itap of a {}.',
'graffiti of the {}.',
'a toy {}.',
'itap of my {}.',
'a photo of a cool {}.',
'a photo of a small {}.',
'a tattoo of the {}.',
]
def zeroshot_classifier(classnames, templates,model):
with torch.no_grad():
zeroshot_weights = []
for classname in classnames:
texts = [template.format(classname) for template in templates] #format with class
texts = clip.tokenize(texts).cuda() #tokenize
class_embeddings = model.encode_text(texts) #embed with text encoder
class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
class_embedding = class_embeddings.mean(dim=0)
class_embedding /= class_embedding.norm()
zeroshot_weights.append(class_embedding)
zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
return zeroshot_weights
def GetDt(classnames,model):
text_features=zeroshot_classifier(classnames, imagenet_templates,model).t()
dt=text_features[0]-text_features[1]
dt=dt.cpu().numpy()
return dt
|