File size: 2,953 Bytes
47d8ad6 | 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 | """Low-RAM CoreML runtime parity: load a saved mlpackage, drive it statefully
frames 1..16 from zero state, compare CoreML outputs to the CACHED eager
targets (warmup f3, steady f16). No torch-eager forward in the loop and no
checkpoint load (synth inputs are weight-independent), so peak RAM stays well
under the 16GB that the in-process double-run in export_fp32.py blows past.
Usage: python verify_coreml_lean.py <mlpackage> [compute_units]
compute_units: CPU_ONLY (default) | CPU_AND_GPU
"""
import sys
import numpy as np
import torch
import common
import dense_wrapper as dw
def main():
pkg = sys.argv[1] if len(sys.argv) > 1 else "dense_sam3_trackstep_fp32.mlpackage"
cu = sys.argv[2] if len(sys.argv) > 2 else "CPU_ONLY"
cache = torch.load("eager_cache.pt", weights_only=False)
# Random-weight model ONLY for weight-independent input generation
# (synth features + embedding size). Avoids a second 457-tensor load.
model = common.build_model(load_checkpoint=False)
import coremltools as ct
mlmodel = ct.models.MLModel(pkg, compute_units=getattr(ct.ComputeUnit, cu))
in_names = [i.name for i in mlmodel.input_description._fd_spec]
state = mlmodel.make_state()
def rel(c, t):
t = t.float()
c = c.float()
return ((c - t).abs().max() / t.abs().max().clamp_min(1e-9)).item()
checks = {3: cache["warmup"], 16: cache["target"]}
worst = 0.0
appear_flips = 0
print(f"compute_units={cu} pkg={pkg}")
for f in range(1, 17):
fi = dw.frame_inputs(model, cache, f)
feed = {n: v.numpy().astype(np.float32) for n, v in zip(in_names, fi)}
got = mlmodel.predict(feed, state=state)
if f in checks:
ref = checks[f]
by_shape = {tuple(np.asarray(v).shape): torch.from_numpy(
np.asarray(v)).float() for v in got.values()}
t_low = ref["pred_masks"]
t_osl = ref["object_score_logits"]
t_iou = ref["ious"]
c_low = by_shape[tuple(t_low.shape)]
c_osl = by_shape[tuple(t_osl.shape)]
c_iou = by_shape[tuple(t_iou.shape)]
r_low, r_osl, r_iou = rel(c_low, t_low), rel(c_osl, t_osl), rel(c_iou, t_iou)
sign = ((c_low > 0) == (t_low > 0)).float().mean().item()
flip = not torch.equal((c_osl > 0), (t_osl > 0))
appear_flips += int(flip)
worst = max(worst, r_low, r_osl, r_iou)
tag = "warmup" if f == 3 else "steady"
print(f"frame {f:>2} ({tag}): low_rel={r_low:.2e} osl_rel={r_osl:.2e} "
f"iou_rel={r_iou:.2e} sign_agree={sign:.4f} appear_flip={flip}")
thr = 5e-2 if "fp16" in pkg.lower() or cu == "CPU_AND_GPU" else 1e-3
print(f"worst rel: {worst:.3e} appear_flips: {appear_flips} thr: {thr:.0e}")
print("COREML RUNTIME:", "PASS" if worst < thr and appear_flips == 0 else "CHECK")
if __name__ == "__main__":
main()
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