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"""Pure-PyTorch fp32 re-implementation of the three deployed StreamPETR graphs with per-module taps.
Ported from the verified research reference (``research/streampetr/scripts/torch_reference.py``: PCC >= 0.9999999998
against ONNX Runtime on every output, SPEC section 6) onto this port's weight loader (``reference.weights``: every
parameter and constant addressed by its consuming ONNX node). Module boundaries follow the training code (AWML
``projects/StreamPETR``: VoVNet-99-eSE + CPFPN, PETR position embedding, StreamPETRHead with the PETRTemporal
decoder) so that each TT stage has a reference tap. No mmdet / mmcv / mmdet3d; plain ``torch.nn.functional``.
``Backbone(img)``: ``img`` [1, 5, 3, 480, 640] (normalised, ``host.preprocess``) -> ``img_feats`` [1, 5, 256, 30, 40].
``PositionEmbedding(img_metas_pad, intrinsics, img2lidar)`` -> ``pos_embed`` [1, 6000, 256], ``cone`` [1, 6000, 8].
``Head(x, pos_embed, cone, E, Einv, pre)``: the ONNX head (global-frame memory) -> its 14 outputs (ONNX names) plus
``topk_idx`` [1, 256, 1] and ``rec_score`` [1, 900, 1] (not ONNX outputs). The stages are separate methods
(``tokens``, ``memory_pre_update``, ``temporal``, ``queries``, ``decoder``, ``predict``, ``post_update``) so that
``reference.port_form`` can run the TT port's re-parameterised memory through the same functions.
Tap names (``ttaw.golden.TapRegistry``; the device PCC gates use the same names):
================================ ====================== ===================================================
tap shape where
================================ ====================== ===================================================
``backbone.stem`` [5, 128, 120, 160] stem_1 (s2) .. stem_3 (s2), conv + ReLU
``backbone.stage2.osa1`` ... [5, C, h, w] every OSA block output (after eSE and the identity)
``backbone.stage2`` .. ``stage5`` [5, 256..1024, ...] stage outputs (stage4 = 30x40, stage5 = 15x20)
``neck.lateral0`` / ``lateral1`` [5, 256, 30, 40] / 15x20 CPFPN 1x1 laterals (no activation)
``neck.merged`` [5, 256, 30, 40] lateral0 + nearest x2(lateral1)
``img_feats`` [1, 5, 256, 30, 40] fpn_convs.0 3x3 (the backbone graph output)
``pe.coords`` [1, 6000, 192] normalised 3-D points of the 64 depth bins
``pe.hidden`` [1, 6000, 1024] ReLU(Linear 192 -> 1024) of inverse_sigmoid(coords)
``pos_embed`` / ``cone`` [1, 6000, 256] / 8 the PE graph outputs (once per calibration)
``head.tokens`` [1, 6000, 256] x permuted to camera-major tokens
``head.memory_embed`` [1, 6000, 256] memory_embed MLP
``head.sa_gamma`` / ``sa_beta`` [1, 6000, 256] spatial-alignment MLN coefficients (cone only)
``head.memory`` [1, 6000, 256] LN(memory) * gamma + beta: the cross-attention values
``head.pos`` [1, 6000, 256] pos_embed * sigmoid(featurized PE): + memory = keys
``head.mem_ego`` / ``mem_ref`` [1, 1024, 4, 4] / 3 Einv @ pre_memory_egopose / reference points
``head.temp_ref`` [1, 1024, 3] (mem_ref - pc_min) / pc_size
``head.motion`` [1, 1024, 180] nerf_pe([velo, ts, mem_ego[:3]])
``head.temp_pos`` / ``temp_mem`` [1, 1024, 256] after the two MLNs and the time embedding
``head.tgt`` / ``query_pos`` [1, 900, 256] decoder inputs (644 learned + 256 propagated)
``head.reference_points`` [1, 900, 3]
``head.layer<l>.self_attn`` [1, 900, 256] LN(tgt + self-attention), l = 0..5
``head.layer<l>.cross_attn`` [1, 900, 256] LN(. + cross-attention over the 6000 tokens)
``head.layer<l>.ffn`` [1, 900, 256] LN(. + FFN): the next layer's tgt
``head.outs_dec`` [6, 1, 900, 256] post_norm of every layer's tgt
``head.cls`` / ``head.reg`` [6, 1, 900, 5] / 10 shared branches; reg centres decoded to metres
``head.rec_score`` [1, 900, 1] max_c sigmoid(cls[5]) (TopK k=1)
``head.topk_idx`` [1, 256, 1] TopK k=256 (sorted, ties to the lower index)
``all_cls_scores`` ... ONNX outputs ``Head.__call__`` return values (ONNX names)
================================ ====================== ===================================================
"""
from __future__ import annotations
from typing import Any, Dict, Optional, Tuple
import numpy as np
from ..ttaw.golden import NULL_TAPS, TapRegistry
from .config import StreamPETRConfig, default_config
from .weights import OSA_STAGES, StreamPETRWeights
__all__ = ["Backbone", "PositionEmbedding", "Head", "StreamPETRNet", "inverse_sigmoid", "nerf_pe", "posemb",
"HEAD_OUTPUTS", "MEMORY_KEYS"]
HEAD_OUTPUTS = ("all_cls_scores", "all_bbox_preds", "post_memory_embedding", "post_memory_reference_point",
"post_memory_timestamp", "post_memory_egopose", "post_memory_velo", "reference_points", "tgt",
"temp_memory", "temp_pos", "query_pos", "query_pos_in", "outs_dec")
MEMORY_KEYS = ("embedding", "reference_point", "timestamp", "egopose", "velo")
def _torch():
import torch
return torch
def _t(a: Any):
"""numpy / torch -> float32 torch tensor (own copy for numpy input)."""
torch = _torch()
if isinstance(a, torch.Tensor):
return a.float()
return torch.from_numpy(np.array(a, dtype=np.float32))
def inverse_sigmoid(x: Any, eps: float = 1e-4):
"""``misc.py:7-25`` as the ONNX graphs compute it: clip(x, 0, 1); log(max(x, eps) / max(1 - x, eps))."""
x = x.clamp(0, 1)
return (x.clamp(min=eps) / (1 - x).clamp(min=eps)).log()
def nerf_pe(x: Any, n: int = 6):
"""[sin(x), cos(x), sin(2x), cos(2x), ..., sin(32x), cos(32x)] along the last axis (ONNX /Concat_20)."""
torch = _torch()
enc = [torch.sin(x), torch.cos(x)]
for f in (2.0, 4.0, 8.0, 16.0, 32.0)[: n - 1]:
xf = x * f
enc += [torch.sin(xf), torch.cos(xf)]
return torch.cat(enc, -1)
def posemb(p: Any, dim_t: Any):
"""One axis of ``pos2posemb``: ``q = p / dim_t``, then (sin of the even, cos of the odd entries) interleaved."""
torch = _torch()
q = p[..., None] / dim_t
return torch.stack((q[..., 0::2].sin(), q[..., 1::2].cos()), -1).flatten(-2)
# ============================================================================================== backbone
class Backbone:
"""VoVNet-99-eSE + CPFPN (``vovnet.py:80-88,174-239``, ``cp_fpn.py:160-204``), BN folded by the exporter."""
def __init__(self, weights: StreamPETRWeights, cfg: Optional[StreamPETRConfig] = None):
self.cfg = cfg or default_config()
self.params: Dict[str, Tuple[Any, Any, int, int]] = {
n: (_t(c.weight), None if c.bias is None else _t(c.bias), c.stride, c.padding)
for n, c in weights.iter_convs()}
def conv(self, x: Any, node: str, relu: bool = True):
import torch.nn.functional as F
w, b, s, p = self.params[node]
y = F.conv2d(x, w, b, stride=s, padding=p)
return F.relu(y) if relu else y
def osa(self, x: Any, stage: int, k: int, identity: bool):
"""One OSA block: 5 chained 3x3 convs, concat [x, 5 outputs], 1x1 concat conv, eSE, optional identity."""
torch = _torch()
pre = f"/img_backbone/stage{stage}/OSA{stage}_{k}"
outs, h = [x], x
for i in range(5):
h = self.conv(h, f"{pre}/layers.{i}/OSA{stage}_{k}_{i}/conv/Conv")
outs.append(h)
xt = self.conv(torch.cat(outs, 1), f"{pre}/concat/OSA{stage}_{k}_concat/conv/Conv")
# eSE (vovnet.py:174-186): x * hsigmoid(fc(GAP(x))), hsigmoid = clip(v + 3, 0, 6) / 6
s = self.conv(xt.mean((2, 3), keepdim=True), f"{pre}/ese/fc/Conv", relu=False)
xt = xt * (torch.clamp(s + 3.0, 0.0, 6.0) / 6.0)
return xt + x if identity else xt
def __call__(self, img: Any, taps: TapRegistry = NULL_TAPS):
"""``img`` [1, N, 3, H, W] (or [N, 3, H, W]) normalised -> ``img_feats`` [1, N, 256, H/16, W/16]."""
import torch.nn.functional as F
x = _t(img)
if x.dim() == 4:
x = x[None]
b, n = x.shape[:2]
x = x.reshape(b * n, *x.shape[2:])
for i in (1, 2, 3):
x = self.conv(x, f"/img_backbone/stem/stem_{i}/conv/Conv")
taps.tap("backbone.stem", x)
feats = {}
for st, (nb, _, _) in OSA_STAGES.items():
if st != 2:
x = F.max_pool2d(x, 3, 2, ceil_mode=True) # MaxPool k3 s2 pads 0 ceil_mode 1
for k in range(1, nb + 1):
x = taps.tap(f"backbone.stage{st}.osa{k}", self.osa(x, st, k, identity=k > 1))
feats[st] = taps.tap(f"backbone.stage{st}", x)
lat0 = taps.tap("neck.lateral0", self.conv(feats[4], "/img_neck/lateral_convs.0/conv/Conv", relu=False))
lat1 = taps.tap("neck.lateral1", self.conv(feats[5], "/img_neck/lateral_convs.1/conv/Conv", relu=False))
# ONNX Resize nearest / asymmetric / floor to [5, 256, 30, 40] == F.interpolate(mode="nearest")
merged = taps.tap("neck.merged", lat0 + F.interpolate(lat1, size=lat0.shape[-2:], mode="nearest"))
out = self.conv(merged, "/img_neck/fpn_convs.0/conv/Conv", relu=False)
return taps.tap("img_feats", out.reshape(b, n, *out.shape[1:]))
# ============================================================================================== position embedding
class PositionEmbedding:
"""PETR 3-D position embedding (``streampetr_head.py:415-463``, ``containers.py:11-24``), run once per
calibration. Constants (pixel centres, LID depth bins, ``position_range``) are read from the ONNX graph."""
def __init__(self, weights: StreamPETRWeights, cfg: Optional[StreamPETRConfig] = None):
self.cfg = cfg or default_config()
pe = weights.pe
self.l0 = tuple(_t(a) for a in weights.linear("/position_encoder/position_encoder.0", "position_embedding"))
self.l2 = tuple(_t(a) for a in weights.linear("/position_encoder/position_encoder.2", "position_embedding"))
self.u = _t(pe.params("/Div")[0]) # [40] pixel centres 16 x + 8 (divided by pad_w in-graph)
self.v = _t(pe.params("/Div_1")[0]) # [30]
depth = np.asarray(pe.array("/Tile_3_output_0"), np.float32) # [1, 6000, 64, 1], equal rows
if not np.all(depth == depth[:, :1]):
raise ValueError("the PE depth-bin constant differs between tokens")
self.depth = _t(depth[0, 0, :, 0]) # [64] LID bins 1.0 .. 59.35 m
self.depth_min = float(np.asarray(pe.array("/Constant_71_output_0")).reshape(-1)[0]) # 1e-5
self.pr_min = _t(pe.params("/Sub")[1]) # position_range[:3]
self.pr_size = _t(pe.params("/Div_3")[1]) # position_range[3:] - [:3]
self.focal_div = float(pe.params("/Div_2")[1]) # 1000.0
def __call__(self, img_metas_pad: Any, intrinsics: Any, img2lidar: Any, taps: TapRegistry = NULL_TAPS):
"""``img_metas_pad`` [3] = (pad_h, pad_w, 3); ``intrinsics`` / ``img2lidar`` [1, N, 4, 4] float32 ->
``pos_embed`` [1, N*H*W, 256], ``cone`` [1, N*H*W, 8]."""
torch = _torch()
metas, k4, i2l = _t(img_metas_pad), _t(intrinsics), _t(img2lidar)
h, w = self.cfg.feat_hw
n = k4.shape[1]
pad_h, pad_w = metas[0], metas[1]
xs = (self.u / pad_w) * pad_w # the graph's (u / pad_w) * pad_w, in float32
ys = (self.v / pad_h) * pad_h
sy, sx = torch.meshgrid(ys, xs, indexing="ij")
loc = torch.stack([sx, sy], -1) # [30, 40, 2] (u, v)
centers = loc[None].repeat(n, 1, 1, 1).reshape(1, n * h * w, 1, 2) # camera-major tokens
dn = self.depth.numel()
d = self.depth.view(1, 1, dn, 1).expand(1, n * h * w, dn, 1)
coords = torch.cat([centers.expand(1, n * h * w, dn, 2), d, torch.ones_like(d)], -1)
coords = torch.cat([coords[..., :2] * torch.clamp(coords[..., 2:3], min=self.depth_min), coords[..., 2:]], -1)
m = i2l.reshape(n, 1, 1, 4, 4).expand(n, h * w, dn, 4, 4).reshape(1, n * h * w, dn, 4, 4)
c3d = (m @ coords.unsqueeze(-1)).squeeze(-1)[..., :3]
c3d = ((c3d - self.pr_min) / self.pr_size).reshape(1, n * h * w, dn * 3)
taps.tap("pe.coords", c3d)
hid = taps.tap("pe.hidden", torch.relu(inverse_sigmoid(c3d) @ self.l0[0] + self.l0[1]))
pos = hid @ self.l2[0] + self.l2[1]
# upstream quirk (streampetr_head.py:419-421): the focal lengths are TILED, token t uses camera t % N
f = torch.stack([k4[..., 0, 0], k4[..., 1, 1]], -1).abs() / self.focal_div
f = f.repeat(1, h * w, 1)
cone = torch.cat([f, c3d[..., -3:], c3d[..., -90:-87]], -1)
return taps.tap("pos_embed", pos), taps.tap("cone", cone)
# ============================================================================================== head
class Head:
"""StreamPETRHead + PETRTemporalTransformer decoder exactly as ``simplify_pts_head_memory.onnx`` (S:4.3)."""
def __init__(self, weights: StreamPETRWeights, cfg: Optional[StreamPETRConfig] = None):
self.cfg = cfg or default_config()
lin = lambda p: tuple(_t(a) for a in weights.linear(p)) # noqa: E731
ln = lambda n: (lambda g, b, e: (_t(g), _t(b), e))(*weights.layer_norm(n)) # noqa: E731
self.memory_embed = (lin("/memory_embed/memory_embed.0"), lin("/memory_embed/memory_embed.2"))
self.sa = dict(ln=ln("/spatial_alignment/ln/LayerNormalization"), reduce=lin("/spatial_alignment/reduce/reduce.0"),
gamma=lin("/spatial_alignment/gamma"), beta=lin("/spatial_alignment/beta"))
self.fpe = (lin("/featurized_pe/conv_reduce"), lin("/featurized_pe/conv_expand"))
self.qemb = (lin("/query_embedding/query_embedding.0_1"), lin("/query_embedding/query_embedding.2_1"))
self.mln_pe = dict(ln=ln("/ego_pose_pe/ln_1/LayerNormalization"), reduce=lin("/ego_pose_pe/reduce/reduce.0_1"),
gamma=lin("/ego_pose_pe/gamma_1"), beta=lin("/ego_pose_pe/beta_1"))
self.mln_mem = dict(ln=ln("/ego_pose_memory/ln_1/LayerNormalization"),
reduce=lin("/ego_pose_memory/reduce/reduce.0_1"),
gamma=lin("/ego_pose_memory/gamma_1"), beta=lin("/ego_pose_memory/beta_1"))
self.time = (lin("/time_embedding/time_embedding.0_1"),
ln("/time_embedding/time_embedding.1_1/LayerNormalization"))
self.scale = float(weights.attention_scale())
self.layers = []
for li in range(self.cfg.num_layers):
p = f"/transformer/decoder/layers.{li}"
attn = []
for a in range(2):
q = f"{p}/attentions.{a}"
attn.append({k: tuple(_t(v) for v in weights.attn_proj(q, k)) for k in ("q", "k", "v")}
| {"o": tuple(_t(v) for v in weights.gemm(f"{q}/attn/Gemm"))})
self.layers.append(dict(attn=attn, norms=[ln(f"{p}/norms.{i}/LayerNormalization") for i in range(3)],
ffn=(lin(f"{p}/ffns.0/layers/layers.0/layers.0.0"),
lin(f"{p}/ffns.0/layers/layers.1"))))
self.post_norm = ln("/transformer/decoder/post_norm/LayerNormalization")
self.cls = (lin("/cls_branches.0/cls_branches.0.0"), ln("/cls_branches.0/cls_branches.0.1/LayerNormalization"),
lin("/cls_branches.0/cls_branches.0.3"), ln("/cls_branches.0/cls_branches.0.4/LayerNormalization"),
lin("/cls_branches.0/cls_branches.0.6"))
self.reg = (lin("/reg_branches.0/reg_branches.0.0"), lin("/reg_branches.0/reg_branches.0.2"),
lin("/reg_branches.0/reg_branches.0.4"))
q = weights.learned_queries()
self.ref644, self.tgt644 = _t(q["reference_points"]), _t(q["tgt"])
self.qpos644, self.query_pos_in = _t(q["query_pos"]), _t(q["query_pos_in"])
h = weights.head
self.pc_min = _t(h.params("/Sub")[1]) # pc_range[:3] (temporal alignment)
self.pc_size = _t(h.params("/Div_3")[1]) # pc_range[3:] - [:3]
self.two_pi = float(h.params("/Mul_1")[1]) # float32(2 pi)
self.dim_t3 = _t(h.params("/Div_4")[1]) # [128] pos2posemb3d temperatures
self.dim_t1 = _t(h.params("/Div_7")[1]) # [256] pos2posemb1d
self.box_scale = _t(h.params("/Mul_69")[1]) # pc_range size (box centre decode)
self.box_min = _t(h.params("/Add_8")[1]) # pc_range[:3]
# ---- primitives -------------------------------------------------------------------------------------
@staticmethod
def lin(x: Any, wb: Tuple[Any, Any]):
return x @ wb[0] + wb[1]
@staticmethod
def layer_norm(x: Any, p: Tuple[Any, Any, float]):
import torch.nn.functional as F
return F.layer_norm(x, (x.shape[-1],), p[0], p[1], p[2])
def mln(self, x: Any, c: Any, p: Dict[str, Any]):
"""MLN (``petr_transformer.py`` / ``misc.py:155-216``): gamma(c) * LN(x) + beta(c), c = ReLU(reduce(c))."""
torch = _torch()
x = self.layer_norm(x, p["ln"])
c = torch.relu(self.lin(c, p["reduce"]))
return self.lin(c, p["gamma"]) * x + self.lin(c, p["beta"])
def mha(self, q_in: Any, k_in: Any, v_in: Any, p: Dict[str, Any]):
"""nn.MultiheadAttention as exported: Q scaled after the projection, softmax over keys, Gemm out-proj."""
torch = _torch()
lq, lk, c = q_in.shape[1], k_in.shape[1], q_in.shape[2]
nh = self.cfg.num_heads
hd = c // nh
q = self.lin(q_in, p["q"]).view(lq, nh, hd).transpose(0, 1) * self.scale
k = self.lin(k_in, p["k"]).view(lk, nh, hd).transpose(0, 1)
v = self.lin(v_in, p["v"]).view(lk, nh, hd).transpose(0, 1)
a = torch.softmax(q @ k.transpose(1, 2), -1)
o = (a @ v).transpose(0, 1).reshape(1, lq, c)
return self.lin(o, p["o"])
def posemb3d(self, p: Any):
"""``pos2posemb3d`` (``positional_encoding.py``): 128 features per axis, concatenated in (y, x, z) order."""
torch = _torch()
q = p * self.two_pi
x, y, z = (posemb(q[..., i], self.dim_t3) for i in range(3))
return torch.cat((y, x, z), -1)
def posemb1d(self, p: Any):
return posemb(p[..., 0] * self.two_pi, self.dim_t1)
def cls_branch(self, x: Any):
torch = _torch()
x = torch.relu(self.layer_norm(self.lin(x, self.cls[0]), self.cls[1]))
x = torch.relu(self.layer_norm(self.lin(x, self.cls[2]), self.cls[3]))
return self.lin(x, self.cls[4])
def reg_branch(self, x: Any):
torch = _torch()
x = torch.relu(self.lin(x, self.reg[0]))
x = torch.relu(self.lin(x, self.reg[1]))
return self.lin(x, self.reg[2])
# ---- stages -----------------------------------------------------------------------------------------
def spatial_alignment_coeffs(self, cone: Any) -> Tuple[Any, Any]:
"""gamma / beta of the spatial-alignment MLN: functions of ``cone`` only (constant per calibration)."""
torch = _torch()
c = torch.relu(self.lin(_t(cone), self.sa["reduce"]))
return self.lin(c, self.sa["gamma"]), self.lin(c, self.sa["beta"])
def tokens(self, x: Any, pos_embed: Any, cone: Any, taps: TapRegistry = NULL_TAPS,
sa_coeffs: Optional[Tuple[Any, Any]] = None) -> Tuple[Any, Any]:
"""Image tokens (``containers.py:76-87``): memory = MLN_sa(memory_embed(x tokens), cone); pos = pos_embed *
sigmoid(featurized PE(memory)). ``sa_coeffs`` = precomputed (gamma, beta) (the port's calibration cache)."""
torch = _torch()
x = _t(x)
b, n, c, h, w = x.shape
memory = taps.tap("head.tokens", x.permute(0, 1, 3, 4, 2).reshape(b, n * h * w, c))
memory = torch.relu(self.lin(memory, self.memory_embed[0]))
memory = taps.tap("head.memory_embed", self.lin(memory, self.memory_embed[1]))
gamma, beta = sa_coeffs if sa_coeffs is not None else self.spatial_alignment_coeffs(cone)
taps.tap("head.sa_gamma", gamma)
taps.tap("head.sa_beta", beta)
memory = taps.tap("head.memory", gamma * self.layer_norm(memory, self.sa["ln"]) + beta)
gate = torch.sigmoid(self.lin(torch.relu(self.lin(memory, self.fpe[0])), self.fpe[1]))
return memory, taps.tap("head.pos", _t(pos_embed) * gate)
@staticmethod
def memory_pre_update(pre: Dict[str, Any], Einv: Any, taps: TapRegistry = NULL_TAPS) -> Tuple[Any, Any]:
"""``containers.py:154-162``: mem_ego = Einv @ pre_egopose; mem_ref = (Einv @ [pre_ref, 1])[:3]."""
torch = _torch()
einv = _t(Einv)
mem_ego = einv[:, None] @ _t(pre["pre_memory_egopose"])
ref = _t(pre["pre_memory_reference_point"])
ref_h = torch.cat([ref, torch.ones_like(ref[..., :1])], -1)
mem_ref = (einv[:, None] @ ref_h.unsqueeze(-1)).squeeze(-1)[..., :3]
return taps.tap("head.mem_ego", mem_ego), taps.tap("head.mem_ref", mem_ref)
def temporal(self, mem_ref: Any, mem_ego: Any, mem_ts: Any, mem_emb: Any, mem_velo: Any,
taps: TapRegistry = NULL_TAPS) -> Dict[str, Any]:
"""Temporal alignment (``streampetr_head.py:465-507``) of the 1024 memory rows."""
torch = _torch()
mem_ts, mem_emb, mem_velo = _t(mem_ts), _t(mem_emb), _t(mem_velo)
temp_ref = taps.tap("head.temp_ref", (mem_ref - self.pc_min) / self.pc_size)
temp_pos = self.lin(torch.relu(self.lin(self.posemb3d(temp_ref), self.qemb[0])), self.qemb[1])
motion = taps.tap("head.motion", nerf_pe(torch.cat([mem_velo, mem_ts, mem_ego[..., :3, :].flatten(-2)], -1)))
temp_pos = self.mln(temp_pos, motion, self.mln_pe)
temp_mem = taps.tap("head.temp_mem", self.mln(mem_emb, motion, self.mln_mem))
temp_pos = temp_pos + self.layer_norm(self.lin(self.posemb1d(mem_ts), self.time[0]), self.time[1])
return {"temp_ref": temp_ref, "temp_pos": taps.tap("head.temp_pos", temp_pos), "temp_mem": temp_mem}
def queries(self, t: Dict[str, Any], taps: TapRegistry = NULL_TAPS) -> Dict[str, Any]:
"""644 learned + the first 256 memory rows; rows 256..1023 feed only the self-attention keys / values."""
torch = _torch()
npr = self.cfg.num_propagated
tgt = taps.tap("head.tgt", torch.cat([self.tgt644, t["temp_mem"][:, :npr]], 1))
query_pos = taps.tap("head.query_pos", torch.cat([self.qpos644, t["temp_pos"][:, :npr]], 1))
ref = taps.tap("head.reference_points", torch.cat([self.ref644, t["temp_ref"][:, :npr]], 1))
return {"tgt": tgt, "query_pos": query_pos, "reference_points": ref,
"temp_memory": t["temp_mem"][:, npr:], "temp_pos": t["temp_pos"][:, npr:]}
def decoder(self, tgt: Any, query_pos: Any, temp_mem: Any, temp_pos: Any, memory: Any, pos: Any,
taps: TapRegistry = NULL_TAPS):
"""6 post-norm layers (``petr_transformer.py:646-753``): self-attention over [queries; memory rows 256..],
cross-attention over the 6000 image tokens (keys memory + pos), FFN; returns outs_dec [6, 1, 900, 256]."""
torch = _torch()
outs = []
for li, lp in enumerate(self.layers):
kv = torch.cat([tgt, temp_mem], 1)
kpos = torch.cat([query_pos, temp_pos], 1)
tgt = self.layer_norm(tgt + self.mha(tgt + query_pos, kv + kpos, kv, lp["attn"][0]), lp["norms"][0])
taps.tap(f"head.layer{li}.self_attn", tgt)
tgt = self.layer_norm(tgt + self.mha(tgt + query_pos, memory + pos, memory, lp["attn"][1]), lp["norms"][1])
taps.tap(f"head.layer{li}.cross_attn", tgt)
hidden = self.lin(torch.relu(self.lin(tgt, lp["ffn"][0])), lp["ffn"][1])
tgt = taps.tap(f"head.layer{li}.ffn", self.layer_norm(tgt + hidden, lp["norms"][2]))
outs.append(self.layer_norm(tgt, self.post_norm))
return taps.tap("head.outs_dec", torch.stack(outs))
def predict(self, outs_dec: Any, reference_points: Any, taps: TapRegistry = NULL_TAPS) -> Tuple[Any, Any]:
"""Shared cls / reg branches on every layer; centres = sigmoid(reg[:3] + inverse_sigmoid(ref)) scaled to
pc_range (``streampetr_head.py:626-714``). -> cls [6, 1, 900, 5] logits, reg [6, 1, 900, 10]."""
torch = _torch()
reference = inverse_sigmoid(reference_points)
cls = torch.stack([self.cls_branch(o) for o in outs_dec])
reg = []
for o in outs_dec:
r = self.reg_branch(o)
reg.append(torch.cat([torch.sigmoid(r[..., :3] + reference), r[..., 3:]], -1))
reg = torch.stack(reg)
reg = torch.cat([reg[..., :3] * self.box_scale + self.box_min, reg[..., 3:]], -1)
return taps.tap("head.cls", cls), taps.tap("head.reg", reg)
def select(self, cls: Any, taps: TapRegistry = NULL_TAPS) -> Tuple[Any, Any]:
"""``containers.py:42-71``: rec_score = max_c sigmoid(cls[last]) (TopK k=1), then TopK k=256 over the 900
queries (sorted descending, ties to the lower index as ONNX TopK)."""
torch = _torch()
rec_score = taps.tap("head.rec_score", torch.sigmoid(cls[-1]).max(-1, keepdim=True).values)
# torch.topk on CPU is not stable for ties; a stable descending sort gives the ONNX order
idx = torch.sort(rec_score[..., 0], dim=1, descending=True, stable=True).indices[:, : self.cfg.num_propagated]
return rec_score, taps.tap("head.topk_idx", idx[..., None])
def post_update(self, cls: Any, reg: Any, outs_dec: Any, mem: Dict[str, Any], E: Any,
taps: TapRegistry = NULL_TAPS, topk_idx: Any = None) -> Dict[str, Any]:
"""``containers.py:42-71``: the new 256 rows (outs_dec / reg of the last layer at the top-256), prepended to
the 1024 memory rows; geometry back to the global frame with E. ``topk_idx`` teacher-forces the selection."""
torch = _torch()
rec_score, idx = self.select(cls, taps)
if topk_idx is not None:
idx = torch.as_tensor(np.asarray(topk_idx)).long().reshape(1, -1, 1)
g = lambda t: torch.gather(t, 1, idx.expand(-1, -1, t.shape[-1])) # noqa: E731
rec_ref, rec_mem, rec_velo = g(reg[-1][..., :3]), g(outs_dec[-1]), g(reg[-1][..., -2:])
k = idx.shape[1]
rec_ego = torch.eye(4).expand(1, k, 4, 4) # upstream gathers rows of an identity: always I
e = _t(E)
post_ego = e[:, None] @ torch.cat([rec_ego, mem["mem_ego"]], 1)
pr = torch.cat([rec_ref, mem["mem_ref"]], 1)
post_ref = (e[:, None] @ torch.cat([pr, torch.ones_like(pr[..., :1])], -1).unsqueeze(-1)).squeeze(-1)[..., :3]
return {"post_memory_embedding": torch.cat([rec_mem, _t(mem["mem_emb"])], 1),
"post_memory_reference_point": post_ref,
"post_memory_timestamp": torch.cat([torch.zeros(1, k, 1, dtype=torch.float64),
_t(mem["mem_ts"]).double()], 1),
"post_memory_egopose": post_ego,
"post_memory_velo": torch.cat([rec_velo, _t(mem["mem_velo"])], 1),
"topk_idx": idx, "rec_score": rec_score}
def __call__(self, x: Any, pos_embed: Any, cone: Any, E: Any, Einv: Any, pre: Dict[str, Any],
taps: TapRegistry = NULL_TAPS, topk_idx: Any = None) -> Dict[str, Any]:
"""The head graph: ``pre`` holds the five ``pre_memory_*`` inputs (ONNX names). Returns the 14 ONNX outputs
(``post_memory_timestamp`` float64 as in the graph) + ``topk_idx`` / ``rec_score``."""
mem_ego, mem_ref = self.memory_pre_update(pre, Einv, taps)
memory, pos = self.tokens(x, pos_embed, cone, taps)
t = self.temporal(mem_ref, mem_ego, pre["pre_memory_timestamp"], pre["pre_memory_embedding"],
pre["pre_memory_velo"], taps)
q = self.queries(t, taps)
outs_dec = self.decoder(q["tgt"], q["query_pos"], q["temp_memory"], q["temp_pos"], memory, pos, taps)
cls, reg = self.predict(outs_dec, q["reference_points"], taps)
mem = {"mem_ego": mem_ego, "mem_ref": mem_ref, "mem_emb": pre["pre_memory_embedding"],
"mem_ts": pre["pre_memory_timestamp"], "mem_velo": pre["pre_memory_velo"]}
post = self.post_update(cls, reg, outs_dec, mem, E, taps, topk_idx=topk_idx)
out = {"all_cls_scores": cls.flatten(0, 2).unsqueeze(0).transpose(2, 1),
"all_bbox_preds": reg.flatten(0, 2).unsqueeze(0).transpose(2, 1),
"reference_points": q["reference_points"], "tgt": q["tgt"], "temp_memory": q["temp_memory"],
"temp_pos": q["temp_pos"], "query_pos": q["query_pos"], "query_pos_in": self.query_pos_in,
"outs_dec": outs_dec}
out.update(post)
taps.tap("all_cls_scores", out["all_cls_scores"])
taps.tap("all_bbox_preds", out["all_bbox_preds"])
return out
# ============================================================================================== the three graphs
class StreamPETRNet:
"""The three graphs over one set of weights (``weights_dir`` = the HF camera_streampetr v1.0 files)."""
def __init__(self, weights_dir: Any, cfg: Optional[StreamPETRConfig] = None, *, threads: Optional[int] = None,
check: bool = True):
torch = _torch()
if threads:
torch.set_num_threads(int(threads))
self.cfg = cfg or default_config()
self.weights = StreamPETRWeights(weights_dir, check=check)
self.backbone = Backbone(self.weights, self.cfg)
self.pe = PositionEmbedding(self.weights, self.cfg)
self.head = Head(self.weights, self.cfg)
@staticmethod
def no_grad():
return _torch().no_grad()
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