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ebeed67 | 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 | """Softmax classifier postprocessor.
Used for: animal-classifier, bird-classifier, vehicle-type-v1, and any custom
softmax classifier.
Input: raw logits tensor
Output: {"kind": "classifications", "classifications": [{"class", "score"}]}
"""
import numpy as np
def postprocess_softmax(
predictions: dict,
config: dict,
orig_w: int,
orig_h: int,
scale: float,
pad: tuple[int, int],
) -> dict:
"""Softmax + argmax + label lookup.
When ``config['outputProbabilities']`` is set the model already applies
softmax IN-GRAPH (e.g. the Google AIY Birds classifier) — its raw output is
a probability distribution, so we consume it directly. Re-softmaxing an
already-normalised probability vector is NOT a no-op: it collapses the
distribution toward uniform (the top-1 score craters far below its true
value), which silently defeats every downstream confidence gate.
"""
labels = config.get("labels", [])
# Get first output tensor
raw = np.array(list(predictions.values())[0]).flatten().astype(np.float32)
if config.get("outputProbabilities"):
# Model output is already a softmax distribution — do NOT re-softmax.
probs = raw
else:
# Stable softmax over raw logits.
shifted = raw - np.max(raw)
exps = np.exp(shifted)
probs = exps / np.sum(exps)
# Return top-K classifications — normalization (top-1 + alternates) happens in TypeScript
top_k = min(5, len(probs))
top_indices = np.argsort(probs)[::-1][:top_k]
classifications = []
for idx in top_indices:
idx = int(idx)
score = float(probs[idx])
if score < 0.01:
break
label = labels[idx] if idx < len(labels) else str(idx)
classifications.append({"class": label, "score": round(score, 4)})
return {"kind": "classifications", "classifications": classifications}
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