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optimized scrfd code

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mertalev 2024-06-09 23:03:34 -04:00
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commit 8d2a849edc
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6 changed files with 411 additions and 32 deletions
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39
machine-learning/app/models/facial_recognition/detection.py
View file

@ -1,44 +1,33 @@
from pathlib import Path
from typing import Any
import numpy as np
import onnxruntime as ort
from insightface.model_zoo import RetinaFace
from numpy.typing import NDArray
from app.models.base import InferenceModel
from app.models.session import ort_has_batch_dim, ort_squeeze_outputs
from app.models.transforms import decode_cv2
from app.models.session import ort_has_batch_dim, ort_expand_outputs
from app.models.transforms import decode_pil
from app.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType
from .scrfd import SCRFD
from PIL import Image
from PIL.ImageOps import pad
class FaceDetector(InferenceModel):
depends = []
identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)
def __init__(
self,
model_name: str,
min_score: float = 0.7,
cache_dir: Path | str | None = None,
**model_kwargs: Any,
) -> None:
self.min_score = model_kwargs.pop("minScore", min_score)
super().__init__(model_name, cache_dir, **model_kwargs)
def _load(self) -> ModelSession:
session = self._make_session(self.model_path)
if isinstance(session, ort.InferenceSession) and ort_has_batch_dim(session):
ort_squeeze_outputs(session)
self.model = RetinaFace(session=session)
self.model.prepare(ctx_id=0, det_thresh=self.min_score, input_size=(640, 640))
if isinstance(session, ort.InferenceSession) and not ort_has_batch_dim(session):
ort_expand_outputs(session)
self.model = SCRFD(session=session)
return session
def _predict(self, inputs: NDArray[np.uint8] | bytes, **kwargs: Any) -> FaceDetectionOutput:
inputs = decode_cv2(inputs)
def _predict(self, inputs: NDArray[np.uint8] | bytes | Image.Image, **kwargs: Any) -> FaceDetectionOutput:
inputs = self._transform(inputs)
bboxes, landmarks = self._detect(inputs)
[bboxes], [landmarks] = self.model.detect(inputs, threshold=kwargs.pop("minScore", 0.7))
return {
"boxes": bboxes[:, :4].round(),
"scores": bboxes[:, 4],
@ -48,5 +37,7 @@ class FaceDetector(InferenceModel):
def _detect(self, inputs: NDArray[np.uint8] | bytes) -> tuple[NDArray[np.float32], NDArray[np.float32]]:
return self.model.detect(inputs) # type: ignore
def configure(self, **kwargs: Any) -> None:
self.model.det_thresh = kwargs.pop("minScore", self.model.det_thresh)
def _transform(self, inputs: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
image = decode_pil(inputs)
padded = pad(image, (640, 640), method=Image.Resampling.BICUBIC)
return np.array(padded, dtype=np.uint8)[None, ...]

325
machine-learning/app/models/facial_recognition/scrfd.py Normal file
View file

@ -0,0 +1,325 @@
# Based on InsightFace-REST by SthPhoenix https://github.com/SthPhoenix/InsightFace-REST/blob/master/src/api_trt/modules/model_zoo/detectors/scrfd.py
# Primary changes made:
# 1. Removed CuPy-related code
# 2. Adapted proposal generation to be thread-safe
# 3. Added typing
# 4. Assume RGB input
# 5. Removed unused variables
# Copyright 2021 SthPhoenix
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
# Based on Jia Guo reference implementation at
# https://github.com/deepinsight/insightface/blob/master/detection/scrfd/tools/scrfd.py
from __future__ import division
import cv2
import numpy as np
from numba import njit
from app.schemas import ModelSession
from numpy.typing import NDArray
@njit(cache=True, nogil=True)
def nms(dets, threshold: float = 0.4) -> NDArray[np.float32]:
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= threshold)[0]
order = order[inds + 1]
return np.asarray(keep)
@njit(fastmath=True, cache=True, nogil=True)
def single_distance2bbox(point: NDArray[np.float32], distance: NDArray[np.float32], stride: int) -> NDArray[np.float32]:
"""
Fast conversion of single bbox distances to coordinates
:param point: Anchor point
:param distance: Bbox distances from anchor point
:param stride: Current stride scale
:return: bbox
"""
distance[0] = point[0] - distance[0] * stride
distance[1] = point[1] - distance[1] * stride
distance[2] = point[0] + distance[2] * stride
distance[3] = point[1] + distance[3] * stride
return distance
@njit(fastmath=True, cache=True, nogil=True)
def single_distance2kps(point: NDArray[np.float32], distance: NDArray[np.float32], stride: int) -> NDArray[np.float32]:
"""
Fast conversion of single keypoint distances to coordinates
:param point: Anchor point
:param distance: Keypoint distances from anchor point
:param stride: Current stride scale
:return: keypoint
"""
for ix in range(0, distance.shape[0], 2):
distance[ix] = distance[ix] * stride + point[0]
distance[ix + 1] = distance[ix + 1] * stride + point[1]
return distance
@njit(fastmath=True, cache=True, nogil=True)
def generate_proposals(
score_blob: NDArray[np.float32],
bbox_blob: NDArray[np.float32],
kpss_blob: NDArray[np.float32],
stride: int,
anchors: NDArray[np.float32],
threshold: float,
) -> tuple[NDArray[np.float32], NDArray[np.float32], NDArray[np.float32]]:
"""
Convert distances from anchors to actual coordinates on source image
and filter proposals by confidence threshold.
:param score_blob: Raw scores for stride
:param bbox_blob: Raw bbox distances for stride
:param kpss_blob: Raw keypoints distances for stride
:param stride: Stride scale
:param anchors: Precomputed anchors for stride
:param threshold: Confidence threshold
:return: Filtered scores, bboxes and keypoints
"""
idxs = []
for ix in range(score_blob.shape[0]):
if score_blob[ix][0] > threshold:
idxs.append(ix)
score_out = np.empty((len(idxs), 1), dtype="float32")
bbox_out = np.empty((len(idxs), 4), dtype="float32")
kpss_out = np.empty((len(idxs), 10), dtype="float32")
for i in range(len(idxs)):
ix = idxs[i]
score_out[i] = score_blob[ix]
bbox_out[i] = single_distance2bbox(anchors[ix], bbox_blob[ix], stride)
kpss_out[i] = single_distance2kps(anchors[ix], kpss_blob[ix], stride)
return score_out, bbox_out, kpss_out
@njit(fastmath=True, cache=True, nogil=True)
def filter(
bboxes_list: NDArray[np.float32],
kpss_list: NDArray[np.float32],
scores_list: NDArray[np.float32],
nms_threshold: float = 0.4,
) -> tuple[NDArray[np.float32], NDArray[np.float32]]:
"""
Filter postprocessed network outputs with NMS
:param bboxes_list: List of bboxes (np.ndarray)
:param kpss_list: List of keypoints (np.ndarray)
:param scores_list: List of scores (np.ndarray)
:return: Face bboxes with scores [t,l,b,r,score], and key points
"""
pre_det = np.hstack((bboxes_list, scores_list))
keep = nms(pre_det, threshold=nms_threshold)
det = pre_det[keep, :]
kpss = kpss_list[keep, :]
kpss = kpss.reshape((kpss.shape[0], -1, 2))
return det, kpss
class SCRFD:
def __init__(self, session: ModelSession):
self.session = session
self.center_cache: dict[tuple[int, int], NDArray[np.float32]] = {}
self.nms_threshold = 0.4
self.fmc = 3
self._feat_stride_fpn = [8, 16, 32]
self._num_anchors = 2
def prepare(self, nms_threshold: float = 0.4) -> None:
"""
Populate class parameters
:param nms_threshold: Threshold for NMS IoU
"""
self.nms_threshold = nms_threshold
def detect(
self, imgs: NDArray[np.uint8], threshold: float = 0.5
) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]]]:
"""
Run detection pipeline for provided images
:param img: Raw image as nd.ndarray with HWC shape
:param threshold: Confidence threshold
:return: Face bboxes with scores [t,l,b,r,score], and key points
"""
height, width = imgs.shape[1:3]
blob = self._preprocess(imgs)
net_outs = self._forward(blob)
batch_bboxes, batch_kpss, batch_scores = self._postprocess(net_outs, height, width, threshold)
dets_list = []
kpss_list = []
for e in range(imgs.shape[0]):
if len(batch_bboxes[e]) == 0:
det, kpss = np.zeros((0, 5), dtype="float32"), np.zeros((0, 10), dtype="float32")
else:
det, kpss = filter(batch_bboxes[e], batch_kpss[e], batch_scores[e], self.nms_threshold)
dets_list.append(det)
kpss_list.append(kpss)
return dets_list, kpss_list
@staticmethod
def _build_anchors(
input_height: int, input_width: int, strides: list[int], num_anchors: int
) -> NDArray[np.float32]:
"""
Precompute anchor points for provided image size
:param input_height: Input image height
:param input_width: Input image width
:param strides: Model strides
:param num_anchors: Model num anchors
:return: box centers
"""
centers = []
for stride in strides:
height = input_height // stride
width = input_width // stride
anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
anchor_centers = (anchor_centers * stride).reshape((-1, 2))
if num_anchors > 1:
anchor_centers = np.stack([anchor_centers] * num_anchors, axis=1).reshape((-1, 2))
centers.append(anchor_centers)
return centers
def _preprocess(self, images: NDArray[np.uint8]):
"""
Normalize image on CPU if backend can't provide CUDA stream,
otherwise preprocess image on GPU using CuPy
:param img: Raw image as np.ndarray with HWC shape
:return: Preprocessed image or None if image was processed on device
"""
input_size = tuple(images[0].shape[0:2][::-1])
return cv2.dnn.blobFromImages(images, 1.0 / 128, input_size, (127.5, 127.5, 127.5), swapRB=False)
def _forward(self, blob: NDArray[np.float32]) -> list[NDArray[np.float32]]:
"""
Send input data to inference backend.
:param blob: Preprocessed image of shape NCHW or None
:return: network outputs
"""
return self.session.run(None, {"input.1": blob})
def _postprocess(
self, net_outs: list[NDArray[np.float32]], height: int, width: int, threshold: float
) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]], list[NDArray[np.float32]]]:
"""
Precompute anchor points for provided image size and process network outputs
:param net_outs: Network outputs
:param input_height: Input image height
:param input_width: Input image width
:param threshold: Confidence threshold
:return: filtered bboxes, keypoints and scores
"""
key = (height, width)
if not self.center_cache.get(key):
self.center_cache[key] = self._build_anchors(height, width, self._feat_stride_fpn, self._num_anchors)
anchor_centers = self.center_cache[key]
bboxes, kpss, scores = self._process_strides(net_outs, threshold, anchor_centers)
return bboxes, kpss, scores
def _process_strides(
self, net_outs: list[NDArray[np.float32]], threshold: float, anchors: NDArray[np.float32]
) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]], list[NDArray[np.float32]]]:
"""
Process network outputs by strides and return results proposals filtered by threshold
:param net_outs: Network outputs
:param threshold: Confidence threshold
:param anchor_centers: Precomputed anchor centers for all strides
:return: filtered bboxes, keypoints and scores
"""
batch_size = net_outs[0].shape[0]
bboxes_by_img = []
kpss_by_img = []
scores_by_img = []
for batch in range(batch_size):
scores_strided = []
bboxes_strided = []
kpss_strided = []
for idx, stride in enumerate(self._feat_stride_fpn):
score_blob = net_outs[idx][batch]
bbox_blob = net_outs[idx + self.fmc][batch]
kpss_blob = net_outs[idx + self.fmc * 2][batch]
stride_anchors = anchors[idx]
score_list, bbox_list, kpss_list = generate_proposals(
score_blob,
bbox_blob,
kpss_blob,
stride,
stride_anchors,
threshold,
)
scores_strided.append(score_list)
bboxes_strided.append(bbox_list)
kpss_strided.append(kpss_list)
bboxes_by_img.append(np.concatenate(bboxes_strided, axis=0))
kpss_by_img.append(np.concatenate(kpss_strided, axis=0))
scores_by_img.append(np.concatenate(scores_strided, axis=0))
return bboxes_by_img, kpss_by_img, scores_by_img

4
machine-learning/app/models/session.py
View file

@ -12,12 +12,12 @@ def ort_has_batch_dim(session: ort.InferenceSession) -> bool:
return session.get_inputs()[0].shape[0] == "batch"
def ort_squeeze_outputs(session: ort.InferenceSession) -> None:
def ort_expand_outputs(session: ort.InferenceSession) -> None:
original_run = session.run
def run(output_names: list[str], input_feed: dict[str, NDArray[np.float32]]) -> list[NDArray[np.float32]]:
out: list[NDArray[np.float32]] = original_run(output_names, input_feed)
out = [o.squeeze(axis=0) for o in out]
out = [np.expand_dims(o, axis=0) for o in out]
return out
session.run = run

4
machine-learning/app/models/transforms.py
View file

@ -3,6 +3,7 @@ from typing import IO
import cv2
import numpy as np
from numba import njit
from numpy.typing import NDArray
from PIL import Image
@ -30,10 +31,11 @@ def to_numpy(img: Image.Image) -> NDArray[np.float32]:
return np.asarray(img if img.mode == "RGB" else img.convert("RGB"), dtype=np.float32) / 255.0
@njit(cache=True, fastmath=True, nogil=True)
def normalize(
img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
) -> NDArray[np.float32]:
return np.divide(img - mean, std, dtype=np.float32)
return (img - mean) / std
def get_pil_resampling(resample: str) -> Image.Resampling:

70
machine-learning/poetry.lock generated
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@ -1528,6 +1528,36 @@ files = [
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llvmlite = "==0.42.*"
numpy = ">=1.22,<1.27"
[[package]]
name = "numpy"
version = "1.26.3"
@ -2037,11 +2101,8 @@ description = "ONNX Runtime is a runtime accelerator for Machine Learning models
optional = false
python-versions = "*"
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@ -3572,4 +3632,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.12"
content-hash = "db51ad1e631b569e106927683a13124252bd80974def1f2edbe23ac87d89c461"
content-hash = "a44e079d565fc1166458690ca2dc5826e198cc07ccab0ebaf71b5ab5e0eed150"

1
machine-learning/pyproject.toml
View file

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gunicorn = ">=21.1.0"
huggingface-hub = ">=0.20.1,<1.0"
tokenizers = ">=0.15.0,<1.0"
numba = "^0.59.1"
[tool.poetry.group.dev.dependencies]
mypy = ">=1.3.0"