File size: 3,477 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | """FLOAT32 CoreML convert + trajectory parity from zero state (warmup→steady)."""
import time
import numpy as np
import torch
import common
import dense_wrapper as dw
def zero_state(wrapper):
wrapper.mem_bank.zero_()
wrapper.img_bank.zero_()
wrapper.ptr_bank.zero_()
wrapper.mem_valid.zero_()
wrapper.ptr_valid.zero_()
def main():
cache = torch.load("eager_cache.pt", weights_only=False)
wrapper, model = dw.build_wrapper()
# Export at a mid frame so frame_pos path (dynamic cond tpos) is exercised
inputs = dw.frame_inputs(model, cache, 3)
common.hide_triton_stub()
with torch.no_grad():
try:
ep = torch.export.export(wrapper, inputs)
except Exception as e:
print(f"strict export failed ({type(e).__name__}: {e}); retrying strict=False")
ep = torch.export.export(wrapper, inputs, strict=False)
ep = ep.run_decompositions({})
print("export OK")
import coremltools as ct
from coremltools.converters.mil.frontend.torch.torch_op_registry import (
register_torch_op,
)
from coremltools.converters.mil.frontend.torch.ops import _get_inputs
from coremltools.converters.mil.mil import Builder as mb
@register_torch_op(torch_alias=["where.scalarother"])
def where_scalarother(context, node):
cond, a, b = _get_inputs(context=context, node=node, expected=3)
context.add(mb.select(cond=cond, a=a, b=b), node.name)
t0 = time.time()
mlmodel = ct.convert(
ep,
minimum_deployment_target=ct.target.iOS18,
# Convert/load on CPU; GPU JIT of masked SDPA previously ballooned RAM.
# Deploy path still CPU_AND_GPU via MLModel(..., compute_units=...).
compute_units=ct.ComputeUnit.CPU_ONLY,
compute_precision=ct.precision.FLOAT32,
)
print(f"fp32 convert OK in {time.time()-t0:.1f}s")
mlmodel.save("dense_sam3_trackstep_fp32.mlpackage")
zero_state(wrapper)
in_names = [i.name for i in mlmodel.input_description._fd_spec]
state = mlmodel.make_state()
print(f"{'frame':>5} {'osl_rel':>9} {'iou_rel':>9} {'sign_agree':>10} "
f"{'lowmask_rel':>11} {'t_nan':>5} {'c_nan':>5}")
worst = 0.0
for f in range(1, 11): # warmup→near-steady; enough to prove state+mask
fi = dw.frame_inputs(model, cache, f)
with torch.no_grad():
t_low, t_high, t_osl, t_ious = [x.clone() for x in wrapper(*fi)]
feed = {n: v.numpy().astype(np.float32) for n, v in zip(in_names, fi)}
got = mlmodel.predict(feed, state=state)
by_shape = {tuple(np.asarray(v).shape): torch.from_numpy(
np.asarray(v)).float() for v in got.values()}
c_low = by_shape[tuple(t_low.shape)]
c_osl = by_shape[tuple(t_osl.shape)]
c_ious = by_shape[tuple(t_ious.shape)]
def rel(a, b):
return ((a - b).abs().max() / b.abs().max().clamp_min(1e-9)).item()
sign = ((c_low > 0) == (t_low > 0)).float().mean().item()
r_osl, r_iou, r_low = rel(c_osl, t_osl), rel(c_ious, t_ious), rel(c_low, t_low)
worst = max(worst, r_osl, r_iou, r_low)
print(f"{f:>5} {r_osl:>9.2e} {r_iou:>9.2e} "
f"{sign:>10.4f} {r_low:>11.2e} "
f"{int(t_low.isnan().sum()):>5} {int(c_low.isnan().sum()):>5}")
print(f"worst rel: {worst:.3e}")
print("FP32 TRAJ:", "PASS" if worst < 1e-4 else "CHECK")
if __name__ == "__main__":
main()
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