Datasets:
File size: 1,365 Bytes
9126e0d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | from pathlib import Path
import os,time,json
t=time.time()
print('starting imports',flush=True)
import torch
from transformers import CLIPSegProcessor,CLIPSegForImageSegmentation,AutoProcessor,AutoModelForZeroShotObjectDetection,SamModel,SamProcessor
from PIL import Image,ImageDraw
root=Path(__file__).resolve().parents[1]
print('imports complete',time.time()-t,torch.__version__,flush=True)
assert torch.cuda.is_available()
device='cuda';modeldir=root/'assets/models/clipseg'
p=CLIPSegProcessor.from_pretrained(modeldir,local_files_only=True);m=CLIPSegForImageSegmentation.from_pretrained(modeldir,local_files_only=True).to(device).eval()
im=Image.new('RGB',(320,320),'white');ImageDraw.Draw(im).ellipse((60,60,230,230),fill='red')
x=p(text=['a red circle'],images=[im],return_tensors='pt',padding=True).to(device)
with torch.inference_mode():y=m(**x,interpolate_pos_encoding=True).logits
torch.cuda.synchronize()
result={'job_id':os.environ.get('SLURM_JOB_ID'),'device':torch.cuda.get_device_name(0),'torch':torch.__version__,'shape':list(y.shape),'finite':bool(torch.isfinite(y).all()),'max_probability':float(y.sigmoid().max()),'elapsed_seconds':time.time()-t,'purpose':'Infrastructure smoke test on a constructed image; not a benchmark result'}
(root/'results/pilot.json').write_text(json.dumps(result,indent=2));print(json.dumps(result,indent=2),flush=True)
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