File size: 2,413 Bytes
6483910 |
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 |
from typing import Dict, List, Any
import io
import base64
from PIL import Image
import torch
import open_clip
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.backends.mps.is_available():
device = "mps"
else:
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
class EndpointHandler():
def __init__(self, path='hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K'):
self.tokenizer = open_clip.get_tokenizer(path)
self.model, self.preprocess = open_clip.create_model_from_pretrained(path)
self.model = self.model.to(device)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
classes = data.pop('classes')
base64_image = data.pop('base64_image')
image_data = base64.b64decode(base64_image)
image = Image.open(io.BytesIO(image_data))
image = self.preprocess(image).unsqueeze(0).to(device)
text = self.tokenizer(classes).to(device)
with torch.no_grad():
image_features = self.model.encode_image(image)
text_features = self.model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
return {
"text_probs": text_probs.tolist()[0],
"image_features" : image_features.tolist()[0],
"text_features" : text_features.tolist()[0]
}
if __name__ == "__main__":
handler = EndpointHandler()
# read image from disk and decode to base 64
with open("/Users/mpa/Library/Mobile Documents/com~apple~CloudDocs/mac/work/zillow-scrapper/properties/76031221/1af0f3c34bff2173ab74ae46a5905d4a-cc_ft_1536.jpg", "rb") as f:
image_data = f.read()
base64_image = base64.b64encode(image_data).decode("utf-8")
data = {
"classes": ["bedroom", "kitchen", "bathroom", "living room", "dining room", "patio", "backyard", "front yard", "garage", "pool"],
"base64_image": base64_image
}
results = handler(data)
print('output') |