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# -*- coding: utf-8 -*-
"""Portable NumPy implementation of the BEVFormer NMSFreeCoder decode path."""
from __future__ import annotations
import numpy as np
def _sigmoid_direct(value):
value = np.asarray(value, dtype=np.float32)
one = np.float32(1.0)
return one / (one + np.exp(-value))
def _sigmoid_stable(value):
value = np.asarray(value, dtype=np.float32)
output = np.empty_like(value, dtype=np.float32)
positive = value >= np.float32(0.0)
negative = ~positive
output[positive] = np.float32(1.0) / (
np.float32(1.0) + np.exp(-value[positive])
)
exp_value = np.exp(value[negative])
output[negative] = exp_value / (
np.float32(1.0) + exp_value
)
return output
def _denormalize_bbox(normalized_bboxes):
boxes = np.asarray(normalized_bboxes, dtype=np.float32)
if boxes.ndim != 2:
raise ValueError(
"bbox tensor must be 2D, got {}".format(boxes.shape)
)
if boxes.shape[1] < 8:
raise ValueError(
"bbox code size must be >=8, got {}".format(boxes.shape[1])
)
cx = boxes[:, 0:1]
cy = boxes[:, 1:2]
width = np.exp(boxes[:, 2:3])
length = np.exp(boxes[:, 3:4])
cz = boxes[:, 4:5]
height = np.exp(boxes[:, 5:6])
rotation = np.arctan2(
boxes[:, 6:7],
boxes[:, 7:8],
)
if boxes.shape[1] > 8:
if boxes.shape[1] < 10:
raise ValueError(
"velocity bbox code requires 10 values, got {}".format(
boxes.shape[1]
)
)
velocity_x = boxes[:, 8:9]
velocity_y = boxes[:, 9:10]
return np.ascontiguousarray(
np.concatenate(
[
cx,
cy,
cz,
width,
length,
height,
rotation,
velocity_x,
velocity_y,
],
axis=-1,
),
dtype=np.float32,
)
return np.ascontiguousarray(
np.concatenate(
[
cx,
cy,
cz,
width,
length,
height,
rotation,
],
axis=-1,
),
dtype=np.float32,
)
def decode_numpy_nmsfreecoder(
cls_scores,
bbox_preds,
contract,
sigmoid_mode=None,
sort_kind=None,
precomputed_probabilities=None,
):
num_classes = int(contract["num_classes"])
max_num = int(contract["max_num"])
num_query = int(contract["num_query"])
code_size = int(contract["code_size"])
bbox = np.asarray(bbox_preds, dtype=np.float32).reshape(
num_query,
code_size,
)
if not np.isfinite(bbox).all():
raise ValueError("bbox predictions contain non-finite values")
if precomputed_probabilities is not None:
probabilities = np.asarray(
precomputed_probabilities,
dtype=np.float32,
).reshape(num_query, num_classes)
else:
cls = np.asarray(cls_scores, dtype=np.float32).reshape(
num_query,
num_classes,
)
if not np.isfinite(cls).all():
raise ValueError("classification logits contain non-finite values")
selected_sigmoid = (
sigmoid_mode
or contract.get("selected_sigmoid_mode")
or "direct"
)
if selected_sigmoid == "direct":
probabilities = _sigmoid_direct(cls)
elif selected_sigmoid == "stable":
probabilities = _sigmoid_stable(cls)
else:
raise ValueError(
"unsupported sigmoid mode: {}".format(selected_sigmoid)
)
flattened = np.ascontiguousarray(
probabilities.reshape(-1),
dtype=np.float32,
)
keep_count = min(max_num, flattened.size)
# Use argpartition (quickselect, same family as PyTorch CPU topk) to
# isolate the top-k candidates, then deterministically sort by
# (score descending, original index ascending) so ties are portable.
partition_index = keep_count - 1
candidate_indices = np.argpartition(
-flattened,
partition_index,
kind="introselect",
)[:keep_count]
candidate_scores = flattened[candidate_indices]
# Sort candidates by (-score, index) for deterministic tie-breaking.
sort_order = np.lexsort(
(candidate_indices, -candidate_scores)
)
order = candidate_indices[sort_order]
scores = np.ascontiguousarray(
flattened[order],
dtype=np.float32,
)
labels = np.ascontiguousarray(
order % num_classes,
dtype=np.int64,
)
bbox_indices = order // num_classes
selected_bbox = np.ascontiguousarray(
bbox[bbox_indices],
dtype=np.float32,
)
decoded_boxes = _denormalize_bbox(selected_bbox)
score_threshold = contract.get("score_threshold")
threshold_mask = np.ones(
scores.shape,
dtype=bool,
)
if score_threshold is not None:
threshold = float(score_threshold)
threshold_mask = scores > np.float32(threshold)
temporary_threshold = threshold
while int(np.count_nonzero(threshold_mask)) == 0:
temporary_threshold *= 0.9
if temporary_threshold < 0.01:
threshold_mask = scores > np.float32(-1.0)
break
threshold_mask = scores >= np.float32(
temporary_threshold
)
post_center_range = contract.get("post_center_range")
if post_center_range is None:
raise ValueError(
"post_center_range is required by this BEVFormer contract"
)
post_center = np.asarray(
post_center_range,
dtype=np.float32,
).reshape(-1)
if post_center.size != 6:
raise ValueError(
"post_center_range must contain 6 values"
)
spatial_mask = np.all(
decoded_boxes[:, :3] >= post_center[:3],
axis=1,
)
spatial_mask &= np.all(
decoded_boxes[:, :3] <= post_center[3:],
axis=1,
)
# Match the official implementation's truth-value condition.
if score_threshold:
spatial_mask &= threshold_mask
final_boxes = np.ascontiguousarray(
decoded_boxes[spatial_mask],
dtype=np.float32,
)
final_scores = np.ascontiguousarray(
scores[spatial_mask],
dtype=np.float32,
)
final_labels = np.ascontiguousarray(
labels[spatial_mask],
dtype=np.int64,
)
return final_boxes, final_scores, final_labels
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