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e6f80f3 | 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 | """Configuration-specific target eligibility; never clamp or rewrite labels."""
from __future__ import annotations
import math
import torch
from .objective import supervision_masks
def size_target_conflicts(batch, model_config, loss_config):
"""Report learned, supervised axes outside reference * exp(+-limit).
Complete-field masks follow the criterion. Fixed request dimensions bypass
the learned size head. Missing labels are selected out before log arithmetic;
disabled size/box objectives impose no size-target range requirement.
A 1e-6 log tolerance only handles float32 rounding at the exact endpoints.
"""
masks = supervision_masks(batch)
slots = torch.zeros_like(batch["slot_mask"])
if loss_config.size > 0:
slots = slots | masks["size"]
if loss_config.box > 0:
slots = slots | masks["box"]
fixed = batch.get("fixed_size_mask", torch.zeros_like(batch["validity"]["size"]))
coordinates = slots.unsqueeze(-1) & ~fixed
conflicts = []
for b, i, q in coordinates.nonzero().tolist():
target = float(batch["targets"]["size"][b, i, q])
reference = model_config.size_reference[q]
log_ratio = math.log(target) - math.log(reference)
limit = model_config.size_log_limit
if abs(log_ratio) > limit + 1e-6:
conflicts = conflicts + [{"batch_index": b, "object_id": batch["objects"][b][i]["id"],
"dimension": ("w", "d", "h")[q], "target_m": target,
"minimum_m": reference * math.exp(-limit),
"maximum_m": reference * math.exp(limit)}]
return conflicts
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