| 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) |
|
|
