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"""Weights of ``bevdet_one_lt_d.onnx`` (AutowareFoundation/tensorrt_bevdet@v1.0), read the way the TT port reads them.
The ONNX is parsed as data with the vendored ``ttaw.weights.OnnxWeights`` (C04): nothing is executed, and every
parameter is addressed through the graph node that consumes it (``consumer_of(<initializer>, op)`` -> the node ->
``conv()`` / ``gemm()`` / ``batchnorm()``), so the convolution attributes (strides, pads, dilations, groups) come from
the deployed graph itself, never from re-typed constants. The export folded every BatchNorm2d into its convolution
(``fuse_conv_bn=True``, S:271), so the conv weights are used as stored; the one remaining BatchNorm
(``depth_net.bn`` = BatchNorm1d(27) over ``cam_params``) is folded by the port at calibration time into the camera
gates (``reference.port_form.camera_gates``, float64, one final rounding), exactly where the TT port folds it.
:meth:`BEVDetWeights.check_graph` verifies that the file is the network this port was written for (node coverage,
plugin attributes, resize modes, softmax axis, output names) and fails loudly otherwise.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Tuple
import numpy as np
from .config import HEAD_NAMES, BEVDetConfig
__all__ = ["ONNX_FILE", "ONNX_SHA256", "BEVDetWeights", "module_names", "find_onnx"]
ONNX_FILE = "bevdet_one_lt_d.onnx"
# sha256 of the pinned file (HF LFS sha256 == local, SPEC section 1)
ONNX_SHA256 = "47f156cb63ca97fa1d47b5d46eba648d4bbe71c95a08af14d3d0fdf53d800d77"
HEAD_BRANCHES = ("reg", "height", "dim", "rot", "vel", "heatmap")
def module_names() -> Dict[str, List[str]]:
"""Every convolution / linear module of the network by stage, in graph order (initializer prefixes)."""
backbone = ["img_backbone.conv1"]
for li, nb in enumerate([3, 4, 6, 3], start=1):
for bi in range(nb):
p = f"img_backbone.layer{li}.{bi}"
backbone += [f"{p}.conv1", f"{p}.conv2", f"{p}.conv3"] + ([f"{p}.downsample.0"] if bi == 0 else [])
neck = ["img_neck.lateral_convs.0.conv", "img_neck.lateral_convs.1.conv", "img_neck.fpn_convs.0.conv"]
dn = "img_view_transformer.depth_net."
depthnet = [dn + "reduce_conv.0", dn + "context_se.conv_reduce", dn + "context_se.conv_expand",
dn + "context_conv", dn + "depth_se.conv_reduce", dn + "depth_se.conv_expand"]
for bi in range(3):
depthnet += [dn + f"depth_conv.{bi}.conv1", dn + f"depth_conv.{bi}.conv2"]
depthnet += [dn + f"depth_conv.3.aspp{i}.atrous_conv" for i in range(1, 5)]
depthnet += [dn + "depth_conv.3.global_avg_pool.1", dn + "depth_conv.3.conv1", dn + "depth_conv.4"]
linears = [dn + "context_mlp.fc1", dn + "context_mlp.fc2", dn + "depth_mlp.fc1", dn + "depth_mlp.fc2"]
bev = []
for li in range(3):
for bi in range(2):
p = f"img_bev_encoder_backbone.layers.{li}.{bi}"
bev += [f"{p}.conv1", f"{p}.conv2"] + ([f"{p}.downsample"] if bi == 0 else [])
bev_neck = ["img_bev_encoder_neck.conv.0", "img_bev_encoder_neck.conv.3", "img_bev_encoder_neck.up2.1",
"img_bev_encoder_neck.up2.4"]
head = ["pts_bbox_head.shared_conv.conv"]
for h in HEAD_BRANCHES:
head += [f"pts_bbox_head.task_heads.0.{h}.0.conv", f"pts_bbox_head.task_heads.0.{h}.1"]
return {"img_backbone": backbone, "img_neck": neck, "depth_net": depthnet, "depth_net_linear": linears,
"bev_backbone": bev, "bev_neck": bev_neck, "head": head}
@dataclass(frozen=True)
class Conv:
"""One convolution as the deployed graph runs it (float32 weights as stored)."""
node: str
weight: np.ndarray # [Cout, Cin / groups, kh, kw]
bias: Optional[np.ndarray] # [Cout]
stride: Tuple[int, int]
pads: Tuple[int, int, int, int] # ONNX order: top, left, bottom, right
dilation: Tuple[int, int]
groups: int
@property
def padding(self) -> Tuple[int, int]:
t, l, b, r = self.pads
if (t, l) != (b, r):
raise ValueError(f"{self.node}: asymmetric pads {self.pads}")
return t, l
class BEVDetWeights:
"""Parameters of the deployed network, addressed by consuming node. ``conv(module)`` / ``linear(module)`` take
the module prefix of the training code (e.g. ``img_backbone.layer1.0.conv1``)."""
def __init__(self, onnx_path: Any, *, check: bool = True):
from ..ttaw.weights import OnnxWeights
self.path = Path(onnx_path)
self.onnx = OnnxWeights(self.path)
self._convs: Dict[str, Conv] = {}
if check:
self.check_graph()
@property
def sha256(self) -> str:
return self.onnx.sha256
# ---- addressing ----------------------------------------------------------------------------------
def conv_node(self, module: str) -> str:
return self.onnx.consumer_of(module + ".weight", "Conv").name
def conv(self, module: str) -> Conv:
if module not in self._convs:
node = self.conv_node(module)
p = self.onnx.conv(node)
if p.auto_pad not in ("NOTSET", ""):
raise ValueError(f"{node}: auto_pad {p.auto_pad} is not supported")
self._convs[module] = Conv(node, np.asarray(p.weight, np.float32),
None if p.bias is None else np.asarray(p.bias, np.float32),
tuple(p.strides), tuple(p.pads), tuple(p.dilations), int(p.group))
return self._convs[module]
def linear(self, module: str) -> Tuple[np.ndarray, np.ndarray]:
"""Gemm (``transB=1``, alpha = beta = 1) -> (weight [out, in], bias [out]) float32."""
node = self.onnx.consumer_of(module + ".weight", "Gemm").name
g = self.onnx.gemm(node)
if g.trans_a or not g.trans_b or g.alpha != 1.0 or g.beta != 1.0:
raise ValueError(f"{node}: unexpected Gemm attributes {g}")
return np.asarray(g.weight, np.float32), np.asarray(g.bias, np.float32)
def depth_bn(self):
"""``depth_net.bn``: BatchNorm1d(27) over ``cam_params`` (``ttaw.weights.BatchNorm``, float64)."""
node = self.onnx.consumer_of("img_view_transformer.depth_net.bn.weight", "BatchNormalization").name
return self.onnx.batchnorm(node)
def iter_convs(self) -> Iterator[Tuple[str, Conv]]:
for modules in module_names().values():
for m in modules:
if m.endswith(("fc1", "fc2")):
continue
yield m, self.conv(m)
# ---- graph checks ---------------------------------------------------------------------------------
def check_graph(self, cfg: Optional[BEVDetConfig] = None) -> None:
"""The file is the deployed BEVDet this port reproduces: every Conv / Gemm node maps to exactly one module,
the 4 ``bevdet::`` plugins carry the expected attributes, the resize / softmax semantics are the ones the
reference implements, and the outputs are the 6 head maps + ``curr_bevfeat``."""
from .config import default_config
cfg = cfg or default_config()
w = self.onnx
names = module_names()
conv_modules = [m for k, v in names.items() if k != "depth_net_linear" for m in v]
used = [self.conv_node(m) for m in conv_modules]
all_convs = [n.name for n in w.nodes("Conv")]
if sorted(used) != sorted(all_convs):
raise ValueError(f"{self.path.name}: Conv nodes do not map 1:1 to the BEVDet modules "
f"({len(set(used))} mapped, {len(all_convs)} in the graph)")
gemms = sorted(w.consumer_of(m + ".weight", "Gemm").name for m in names["depth_net_linear"])
if gemms != sorted(n.name for n in w.nodes("Gemm")):
raise ValueError(f"{self.path.name}: Gemm nodes do not map to the 4 camera MLP layers")
pre = w.find_node("*", "Preprocess")
r = float(np.float32(pre.attrs["resize_radio"]))
if (int(pre.attrs["crop_h"]), int(pre.attrs["crop_w"])) != cfg.crop_hw or r != float(cfg.resize):
raise ValueError(f"Preprocess attributes {pre.attrs} differ from the configuration")
bp = w.find_node("*", "BEVPool")
ny, nx = cfg.bev_hw
if (int(bp.attrs["bev_h"]), int(bp.attrs["bev_w"]), int(bp.attrs["n"])) != (ny, nx, cfg.n_cams):
raise ValueError(f"BEVPool attributes {bp.attrs} differ from the configuration")
w.find_node("*", "AlignBEV")
w.find_node("*", "GatherBEV")
modes = [(n.attrs.get("mode"), n.attrs.get("coordinate_transformation_mode"), n.attrs.get("nearest_mode"))
for n in w.nodes("Resize")]
expect = [("nearest", "asymmetric", "floor"), ("linear", "align_corners", "floor"),
("linear", "align_corners", "floor"), ("linear", "align_corners", "floor")]
if modes != expect:
raise ValueError(f"Resize modes {modes} differ from the implemented ones {expect}")
sm = w.nodes("Softmax")
if len(sm) != 1 or int(sm[0].attrs.get("axis", -1)) != 1:
raise ValueError("expected one Softmax over axis 1 (depth bins)")
mp = w.nodes("MaxPool")
if len(mp) != 1 or tuple(mp[0].attrs["kernel_shape"]) != (3, 3) or tuple(mp[0].attrs["strides"]) != (2, 2) \
or tuple(mp[0].attrs["pads"]) != (1, 1, 1, 1) or int(mp[0].attrs.get("ceil_mode", 0)) != 0:
raise ValueError("expected the ResNet stem MaxPool 3x3 s2 p1")
if tuple(w.output_names) != tuple(HEAD_NAMES) + ("curr_bevfeat",):
raise ValueError(f"outputs {w.output_names} differ from {HEAD_NAMES + ('curr_bevfeat',)}")
bn = self.depth_bn()
if bn.channels != cfg.cam_params_size:
raise ValueError("depth_net.bn must have 27 channels")
def find_onnx(weights_dir: Any = None) -> Optional[Path]:
"""``<weights_dir>/bevdet_one_lt_d.onnx`` if it exists (``weights_dir`` may also be the file itself)."""
if weights_dir is None:
return None
p = Path(weights_dir)
if p.is_file():
return p
q = p / ONNX_FILE
return q if q.is_file() else None
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