File size: 8,937 Bytes
fe51a63 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | # SPDX-License-Identifier: Apache-2.0
"""fp32 torch rebuild of the deployed FRNet graph (AWML ``frustum_encoder.py:76-137``, ``frnet_backbone.py:252-356``,
``frnet_head.py:52-70`` in deploy mode), with per-module taps. Ported from the verified research reference
``research/frnet/scripts/frnet_torch_ref.py`` (max |diff| vs ORT 1.6e-5 .. 3.0e-5 on the public goldens), on the
weights as the TT port holds them (``reference/weights.py``: point-layer BatchNorms folded, ``pre_norm`` as x * scale
+ shift).
Input: the four ONNX inputs of one frame, **unpadded** (``host/preprocess.py`` ``NetworkInputs``); the graph sizes
its voxel tensors as ``max(inverse_map) + 1`` (S:frnet:255-262). Every sparse op is deterministic and follows ORT:
- cell mean: index-add of the points / of ones, divided (``ScatterElements(add)`` x2 + ``Div``);
- per-cell max (``ScatterElements(max)``, init ``-FLT_MAX``): ``scatter_reduce(amax, include_self=True)``;
- frustum2pixel (``ScatterND`` into zeros, NHWC -> NCHW): at strides 2 / 4 / 8 several cells fall on one pixel
and ORT applies the updates sequentially, so the **last voxel row** wins (S:frnet:285-287);
- pixel2point (``Gather``): row ``(row // s) * (W // s) + col // s`` of the NHWC map;
- decoder: bilinear ``align_corners=True`` resize of the five maps to H x W, channel concat.
Taps (``ttaw.golden.TapRegistry``; names in ``weights.MODULE_TAPS`` / ``OPT_IN_TAPS``): point tensors ``(N, C)``,
voxel tensors ``(M, C)``, maps ``(1, C, h, w)`` NCHW, all float32 numpy. ``forward`` returns ``pred_probs``
``(N, 27)`` float32. torch is imported here (research venv / tt-env), never by ``api.py`` at module level.
"""
from __future__ import annotations
from typing import Any, Optional
import numpy as np
import torch
import torch.nn.functional as F
from ..ttaw.golden import NULL_TAPS, TapRegistry
from .weights import Conv, FRNetWeights, Linear
__all__ = ["FRNetTorch", "scatter_max", "frustum2pixel", "pixel2point", "TorchLayers"]
def _t(a: np.ndarray) -> torch.Tensor:
a = np.ascontiguousarray(a)
return torch.from_numpy(a if a.flags.writeable else a.copy())
class TorchLayers:
"""The weights as torch tensors (shared by the literal model and the rewrites)."""
def __init__(self, weights: FRNetWeights):
self.w = weights
self.lin = {lin.name: (_t(lin.weight), _t(lin.bias), lin.relu) for lin in weights.linears}
self.conv = {c.name: (_t(c.weight), _t(c.bias), c.stride, c.padding, c.activation) for c in weights.convs}
self.pre_scale = _t(weights.pre_norm_scale)
self.pre_shift = _t(weights.pre_norm_shift)
def linear(self, x: torch.Tensor, layer: Linear) -> torch.Tensor:
w, b, relu = self.lin[layer.name]
y = torch.addmm(b, x, w)
return F.relu(y) if relu else y
def conv2d(self, x: torch.Tensor, layer: Conv, *, activation: bool = True) -> torch.Tensor:
w, b, stride, pad, act = self.conv[layer.name]
y = F.conv2d(x, w, b, stride=stride, padding=pad)
if activation and act == "hardswish":
y = F.hardswish(y)
elif activation and act == "sigmoid":
y = torch.sigmoid(y)
return y
def scatter_max(src: torch.Tensor, inverse_map: torch.Tensor, m: int) -> torch.Tensor:
"""``ScatterElements(reduction=max)`` into ``-FLT_MAX`` rows (every row of a frame owns >= 1 point)."""
out = torch.full((m, src.shape[1]), torch.finfo(src.dtype).min, dtype=src.dtype)
return out.scatter_reduce(0, inverse_map.unsqueeze(-1).expand(-1, src.shape[1]), src, reduce="amax",
include_self=True)
def frustum2pixel(feats: torch.Tensor, voxel_coors: np.ndarray, hw: tuple, stride: int) -> torch.Tensor:
"""``ScatterND`` of voxel rows into a zero ``(1, h, w, C)`` image at ``voxel_coors // stride`` (last voxel wins
on duplicates), returned NCHW."""
H, W = hw
h, w = H // stride, W // stride
lin = (voxel_coors[:, 1] // stride) * w + voxel_coors[:, 2] // stride
last = np.full(h * w, -1, np.int64)
np.maximum.at(last, lin, np.arange(len(lin), dtype=np.int64))
pix = np.flatnonzero(last >= 0)
img = torch.zeros(h * w, feats.shape[1], dtype=feats.dtype)
img[_t(pix)] = feats[_t(last[pix])]
return img.reshape(1, h, w, -1).permute(0, 3, 1, 2).contiguous()
def pixel2point(x: torch.Tensor, coors: np.ndarray, stride: int) -> torch.Tensor:
"""``Gather`` of the NHWC rows of ``x`` (NCHW) at each point's cell ``// stride``."""
_, c, h, w = x.shape
flat = x.permute(0, 2, 3, 1).reshape(h * w, c)
return flat[_t((coors[:, 1] // stride) * w + coors[:, 2] // stride)]
class FRNetTorch:
"""``FRNetTorch(weights)(points, coors, voxel_coors, inverse_map, taps=TapRegistry())`` -> pred_probs."""
def __init__(self, weights: FRNetWeights, *, threads: Optional[int] = None):
if threads:
torch.set_num_threads(int(threads))
self.weights = weights
self.layers = TorchLayers(weights)
def __call__(self, *args: Any, **kwargs: Any) -> np.ndarray:
return self.forward(*args, **kwargs)
@torch.no_grad()
def forward(self, points: np.ndarray, coors: np.ndarray, voxel_coors: np.ndarray, inverse_map: np.ndarray,
taps: TapRegistry = NULL_TAPS) -> np.ndarray:
W, L = self.weights, self.layers
hw = W.frustum_hw
m = int(inverse_map.max()) + 1
voxel_coors = np.asarray(voxel_coors)[:m]
coors = np.asarray(coors)
pts = _t(np.asarray(points, np.float32))
inv = _t(np.asarray(inverse_map, np.int64))
def tap(name: str, value: torch.Tensor) -> torch.Tensor:
if taps.wants(name):
taps.tap(name, value.numpy())
return value
# ---- FrustumFeatureEncoder
dist = torch.linalg.norm(pts[:, :3], dim=1, keepdim=True)
vsum = torch.zeros(m, 4).index_add_(0, inv, pts)
vcnt = torch.zeros(m, 4).index_add_(0, inv, torch.ones_like(pts))
f_cluster = pts[:, :3] - (vsum / vcnt)[inv][:, :3]
x = torch.cat([pts, dist, f_cluster], 1) * L.pre_scale + L.pre_shift
tap("encoder.input", x)
pf = []
for i, lin in enumerate(W.ffe):
x = L.linear(x, lin)
pf.append(tap(f"encoder.pf{i}", x))
vmax = tap("encoder.voxel_max", scatter_max(x, inv, m))
vfeat = tap("encoder.voxel_feats", L.linear(vmax, W.compression))
# ---- backbone: stem, point stem, fusion stem
img = tap("backbone.frustum", frustum2pixel(vfeat, voxel_coors, hw, 1))
for c in W.stem:
img = L.conv2d(img, c)
tap("backbone.stem", img)
p = L.linear(torch.cat([pixel2point(img, coors, 1), pf[-1]], 1), W.point_stem)
tap("backbone.point_stem", p)
seg = tap("backbone.segmax0", scatter_max(p, inv, m))
x = L.conv2d(torch.cat([frustum2pixel(seg, voxel_coors, hw, 1), img], 1), W.fusion_stem)
tap("backbone.fusion_stem", x)
maps, pts_out = [x], [p]
# ---- four stages: BasicBlocks, point fusion, pixel fusion, attention
for st in W.stages:
li, s = st.index, st.stride
for bi, (c1, c2, ds) in enumerate(st.blocks):
y = L.conv2d(L.conv2d(x, c1), c2)
idt = L.conv2d(x, ds) if ds is not None else x
x = tap(f"backbone.layer{li}.block{bi}", F.hardswish(y + idt))
tap(f"backbone.layer{li}", x)
p = L.linear(torch.cat([pixel2point(x, coors, s), p], 1), st.point_fusion)
tap(f"backbone.point_fusion{li}", p)
seg = tap(f"backbone.segmax{li}", scatter_max(p, inv, m))
fuse = L.conv2d(torch.cat([frustum2pixel(seg, voxel_coors, hw, s), x], 1), st.pixel_fusion)
tap(f"backbone.pixel_fusion{li}", fuse)
att = tap(f"backbone.attention{li}", L.conv2d(L.conv2d(fuse, st.attention[0]), st.attention[1]))
x = tap(f"backbone.stage{li}", fuse * att + x)
maps.append(x)
pts_out.append(p)
# ---- decoder (align-corners bilinear up-sampling to H x W, fuse convs) and the point branch
ups = [tap(f"decoder.up{k}", F.interpolate(mp, size=hw, mode="bilinear", align_corners=True))
for k, mp in enumerate(maps)]
y = tap("decoder.fuse1", L.conv2d(torch.cat(ups, 1), W.fuse_layer1))
y = tap("decoder.fuse2", L.conv2d(y, W.fuse_layer2))
q = tap("decoder.point_fuse1", L.linear(torch.cat(pts_out, 1), W.point_fuse[0]))
q = tap("decoder.point_fuse2", L.linear(q, W.point_fuse[1]))
# ---- FRHead
h = pixel2point(y, coors, 1)
for i, (lin, skip) in enumerate(zip(W.head_mlps, (q, pf[2], pf[1], pf[0]))):
h = tap(f"head.mlp{i}", L.linear(h, lin) + skip)
logits = tap("head.logits", L.linear(h, W.conv_seg))
return tap("pred_probs", torch.softmax(logits, -1)).numpy()
|