validate.py

import hashlib,json,time
from pathlib import Path
import torch
torch.set_num_threads(4)
from safetensors.torch import save_file,load_file
from component import load_model
r=Path(__file__).parent;checks=[];start=time.monotonic()
def check(name,ok,**details):
    assert ok,(name,details)
    checks.append({'name':name,'passed':True,**details});print(name,details,flush=True)
src=Path('work/further_unified_1021/UCSC-VLAA--openvision3-vit-base-patch2-32/source.bin')
with src.open('rb') as f:digest=hashlib.file_digest(f,'sha256').hexdigest()
check('official_source_LFS_SHA256',digest=='9d55ca6e9c4bd404dfac552ad65dae673274b28aeffe8f3f00bd7047a1db465e')
original=torch.load(src,map_location='cpu',weights_only=True)
if 'state_dict' in original:original=original['state_dict']
if not (r/'model.safetensors').exists():save_file({k:v.contiguous() for k,v in original.items()},str(r/'model.safetensors'))
selected=load_file(str(r/'model.safetensors'))
hashes={}
for k,v in original.items():
    assert k in selected and torch.equal(v,selected[k]) and v.dtype==selected[k].dtype
    hashes[k]=hashlib.sha256(v.contiguous().reshape(-1).view(torch.uint8).numpy().tobytes()).hexdigest()
check('all_150_tensor_values_and_dtypes',len(original)==len(selected)==150)
(r/'tensor_sha256.json').write_text(json.dumps(hashes,indent=2)+'\n')
del selected,original
m=load_model(r)
check('native_150entry_strict_load',len(m.state_dict())==150)
check('16channel_latent_stem',tuple(m.conv1.weight.shape)==(768,16,2,2))
with torch.inference_mode():
    z=torch.linspace(-1,1,16*32*32).reshape(1,16,32,32)
    pooled,tokens=m(z)
    check('256_patch_token_geometry',tuple(tokens.shape)==(1,256,768) and torch.isfinite(tokens).all().item())
    check('finite_pooled_embedding',tuple(pooled.shape)==(1,768) and torch.isfinite(pooled).all().item())
    direct=m.ln_post(tokens.mean(1))@m.proj
    check('native_average_pool_and_projection',torch.equal(pooled,direct))
    bpool,btokens=m(torch.cat([z,z*.5]))
    check('batched_latent_forward',tuple(btokens.shape)==(2,256,768) and torch.isfinite(bpool).all().item())
    check('batch_independence',torch.allclose(btokens[0],tokens[0],atol=3e-5,rtol=3e-5),max_abs=(btokens[0]-tokens[0]).abs().max().item())
report={'strict_load':True,'source_tensor_bytes_equal':True,'checks':checks,'smoke':{'passed':True,'device':'cpu','dtype':'float32'},'elapsed_seconds':time.monotonic()-start,'limitations':['Latent-encoder test only; it takes 16-channel 32x32 VAE latents, not RGB pixels.','No VAE or diffusion/image decoder exists in this 150-entry checkpoint.','Original RGB-to-latent preprocessing depends on an external FLUX VAE; no such extra checkpoint was downloaded for this test.','Local verification only; checkpoint weights have not been republished while their explicit license is unconfirmed.']}
(r/'validation.json').write_text(json.dumps(report,indent=2)+'\n')
(r/'extraction.json').write_text(json.dumps({'source_repo':'UCSC-VLAA/openvision3-vit-base-patch2-32','source_revision':'7c5fbd37faab5091a1fa29d3ab4f6760d7c87c28','source_file':'open_clip_pytorch_model.bin','source_file_lfs_sha256':digest,'source_tensor_bytes_equal':True,'tensor_count':150,'operation':'Safe weights-only torch.load followed by safetensors serialization; all original trained entries and dtype preserved.'},indent=2)+'\n')
print('OPENVISION LOCAL VALIDATED',len(checks),flush=True)