mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-03-07 02:18:07 +01:00
dbceb4dcc7
* sdk_2.0_update * memryx docs: minor reorg * ran ruff * whoops, more ruff fixes * Fixes (#6) * Fixes and custom model path updated * ruff formatting * removed apt install from main * add comment about libgomp1 in install_deps --------- Co-authored-by: Abinila Siva <abinila.siva@memryx.com> Co-authored-by: Abinila Siva <163017635+abinila4@users.noreply.github.com>
733 lines
28 KiB
Python
733 lines
28 KiB
Python
import glob
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import logging
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import os
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import shutil
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import time
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import urllib.request
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import zipfile
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from queue import Queue
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import cv2
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import numpy as np
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try:
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# from memryx import AsyncAccl # Import MemryX SDK
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from memryx import AsyncAccl
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except ModuleNotFoundError:
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raise ImportError(
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"MemryX SDK is not installed. Install it and set up MIX environment."
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)
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from pydantic import BaseModel, Field
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from typing_extensions import Literal
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import (
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BaseDetectorConfig,
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ModelTypeEnum,
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)
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from frigate.util.model import post_process_yolo
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "memryx"
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# Configuration class for model settings
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class ModelConfig(BaseModel):
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path: str = Field(default=None, title="Model Path") # Path to the DFP file
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labelmap_path: str = Field(default=None, title="Path to Label Map")
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class MemryXDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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device: str = Field(default="PCIe", title="Device Path")
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class MemryXDetector(DetectionApi):
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type_key = DETECTOR_KEY # Set the type key
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supported_models = [
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ModelTypeEnum.ssd,
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ModelTypeEnum.yolonas,
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ModelTypeEnum.yologeneric, # Treated as yolov9 in MemryX implementation
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ModelTypeEnum.yolox,
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]
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def __init__(self, detector_config):
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"""Initialize MemryX detector with the provided configuration."""
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model_cfg = getattr(detector_config, "model", None)
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# Check if model_type was explicitly set by the user
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if "model_type" in getattr(model_cfg, "__fields_set__", set()):
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detector_config.model.model_type = model_cfg.model_type
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else:
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logger.info(
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"model_type not set in config — defaulting to yolonas for MemryX."
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)
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detector_config.model.model_type = ModelTypeEnum.yolonas
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self.capture_queue = Queue(maxsize=10)
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self.output_queue = Queue(maxsize=10)
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self.capture_id_queue = Queue(maxsize=10)
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self.logger = logger
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self.memx_model_path = detector_config.model.path # Path to .dfp file
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self.memx_post_model = None # Path to .post file
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self.expected_post_model = None
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self.memx_device_path = detector_config.device # Device path
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# Parse the device string to split PCIe:<index>
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device_str = self.memx_device_path
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self.device_id = []
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self.device_id.append(int(device_str.split(":")[1]))
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self.memx_model_height = detector_config.model.height
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self.memx_model_width = detector_config.model.width
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self.memx_model_type = detector_config.model.model_type
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self.cache_dir = "/memryx_models"
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if self.memx_model_type == ModelTypeEnum.yologeneric:
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model_mapping = {
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(640, 640): (
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"https://developer.memryx.com/example_files/2p0_frigate/yolov9_640.zip",
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"yolov9_640",
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),
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(320, 320): (
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"https://developer.memryx.com/example_files/2p0_frigate/yolov9_320.zip",
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"yolov9_320",
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),
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}
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self.model_url, self.model_folder = model_mapping.get(
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(self.memx_model_height, self.memx_model_width),
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(
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"https://developer.memryx.com/example_files/2p0_frigate/yolov9_320.zip",
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"yolov9_320",
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),
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)
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self.expected_dfp_model = "YOLO_v9_small_onnx.dfp"
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elif self.memx_model_type == ModelTypeEnum.yolonas:
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model_mapping = {
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(640, 640): (
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"https://developer.memryx.com/example_files/2p0_frigate/yolonas_640.zip",
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"yolonas_640",
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),
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(320, 320): (
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"https://developer.memryx.com/example_files/2p0_frigate/yolonas_320.zip",
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"yolonas_320",
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),
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}
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self.model_url, self.model_folder = model_mapping.get(
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(self.memx_model_height, self.memx_model_width),
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(
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"https://developer.memryx.com/example_files/2p0_frigate/yolonas_320.zip",
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"yolonas_320",
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),
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)
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self.expected_dfp_model = "yolo_nas_s.dfp"
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self.expected_post_model = "yolo_nas_s_post.onnx"
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elif self.memx_model_type == ModelTypeEnum.yolox:
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self.model_folder = "yolox"
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self.model_url = (
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"https://developer.memryx.com/example_files/2p0_frigate/yolox.zip"
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)
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self.expected_dfp_model = "YOLOX_640_640_3_onnx.dfp"
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self.set_strides_grids()
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elif self.memx_model_type == ModelTypeEnum.ssd:
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self.model_folder = "ssd"
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self.model_url = (
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"https://developer.memryx.com/example_files/2p0_frigate/ssd.zip"
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)
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self.expected_dfp_model = "SSDlite_MobileNet_v2_320_320_3_onnx.dfp"
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self.expected_post_model = "SSDlite_MobileNet_v2_320_320_3_onnx_post.onnx"
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self.check_and_prepare_model()
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logger.info(
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f"Initializing MemryX with model: {self.memx_model_path} on device {self.memx_device_path}"
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)
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try:
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# Load MemryX Model
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logger.info(f"dfp path: {self.memx_model_path}")
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# Initialization code
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# Load MemryX Model with a device target
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self.accl = AsyncAccl(
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self.memx_model_path,
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device_ids=self.device_id, # AsyncAccl device ids
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local_mode=True,
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)
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# Models that use cropped post-processing sections (YOLO-NAS and SSD)
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# --> These will be moved to pure numpy in the future to improve performance on low-end CPUs
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if self.memx_post_model:
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self.accl.set_postprocessing_model(self.memx_post_model, model_idx=0)
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self.accl.connect_input(self.process_input)
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self.accl.connect_output(self.process_output)
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logger.info(
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f"Loaded MemryX model from {self.memx_model_path} and {self.memx_post_model}"
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)
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except Exception as e:
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logger.error(f"Failed to initialize MemryX model: {e}")
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raise
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def load_yolo_constants(self):
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base = f"{self.cache_dir}/{self.model_folder}"
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# constants for yolov9 post-processing
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self.const_A = np.load(f"{base}/_model_22_Constant_9_output_0.npy")
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self.const_B = np.load(f"{base}/_model_22_Constant_10_output_0.npy")
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self.const_C = np.load(f"{base}/_model_22_Constant_12_output_0.npy")
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def check_and_prepare_model(self):
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if not os.path.exists(self.cache_dir):
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os.makedirs(self.cache_dir, exist_ok=True)
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# ---------- CASE 1: user provided a custom model path ----------
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if self.memx_model_path:
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if not self.memx_model_path.endswith(".zip"):
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raise ValueError(
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f"Invalid model path: {self.memx_model_path}. "
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"Only .zip files are supported. Please provide a .zip model archive."
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)
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if not os.path.exists(self.memx_model_path):
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raise FileNotFoundError(
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f"Custom model zip not found: {self.memx_model_path}"
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)
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logger.info(f"User provided zip model: {self.memx_model_path}")
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# Extract custom zip into a separate area so it never clashes with MemryX cache
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custom_dir = os.path.join(
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self.cache_dir, "custom_models", self.model_folder
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)
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if os.path.isdir(custom_dir):
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shutil.rmtree(custom_dir)
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os.makedirs(custom_dir, exist_ok=True)
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with zipfile.ZipFile(self.memx_model_path, "r") as zip_ref:
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zip_ref.extractall(custom_dir)
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logger.info(f"Custom model extracted to {custom_dir}.")
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# Find .dfp and optional *_post.onnx recursively
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dfp_candidates = glob.glob(
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os.path.join(custom_dir, "**", "*.dfp"), recursive=True
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)
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post_candidates = glob.glob(
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os.path.join(custom_dir, "**", "*_post.onnx"), recursive=True
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)
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if not dfp_candidates:
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raise FileNotFoundError(
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"No .dfp file found in custom model zip after extraction."
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)
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self.memx_model_path = dfp_candidates[0]
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# Handle post model requirements by model type
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if self.memx_model_type in [
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ModelTypeEnum.yologeneric,
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ModelTypeEnum.yolonas,
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ModelTypeEnum.ssd,
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]:
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if not post_candidates:
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raise FileNotFoundError(
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f"No *_post.onnx file found in custom model zip for {self.memx_model_type.name}."
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)
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self.memx_post_model = post_candidates[0]
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elif self.memx_model_type == ModelTypeEnum.yolox:
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# Explicitly ignore any post model even if present
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self.memx_post_model = None
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else:
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# Future model types can optionally use post if present
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self.memx_post_model = post_candidates[0] if post_candidates else None
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logger.info(f"Using custom model: {self.memx_model_path}")
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return
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# ---------- CASE 2: no custom model path -> use MemryX cached models ----------
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model_subdir = os.path.join(self.cache_dir, self.model_folder)
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dfp_path = os.path.join(model_subdir, self.expected_dfp_model)
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post_path = (
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os.path.join(model_subdir, self.expected_post_model)
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if self.expected_post_model
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else None
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)
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dfp_exists = os.path.exists(dfp_path)
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post_exists = os.path.exists(post_path) if post_path else True
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if dfp_exists and post_exists:
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logger.info("Using cached models.")
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self.memx_model_path = dfp_path
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self.memx_post_model = post_path
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if self.memx_model_type == ModelTypeEnum.yologeneric:
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self.load_yolo_constants()
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return
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# ---------- CASE 3: download MemryX model (no cache) ----------
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logger.info(
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f"Model files not found locally. Downloading from {self.model_url}..."
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)
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zip_path = os.path.join(self.cache_dir, f"{self.model_folder}.zip")
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try:
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if not os.path.exists(zip_path):
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urllib.request.urlretrieve(self.model_url, zip_path)
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logger.info(f"Model ZIP downloaded to {zip_path}. Extracting...")
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if not os.path.exists(model_subdir):
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with zipfile.ZipFile(zip_path, "r") as zip_ref:
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zip_ref.extractall(self.cache_dir)
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logger.info(f"Model extracted to {self.cache_dir}.")
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# Re-assign model paths after extraction
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self.memx_model_path = os.path.join(model_subdir, self.expected_dfp_model)
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self.memx_post_model = (
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os.path.join(model_subdir, self.expected_post_model)
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if self.expected_post_model
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else None
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)
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if self.memx_model_type == ModelTypeEnum.yologeneric:
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self.load_yolo_constants()
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finally:
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if os.path.exists(zip_path):
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try:
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os.remove(zip_path)
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logger.info("Cleaned up ZIP file after extraction.")
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except Exception as e:
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logger.warning(f"Failed to remove downloaded zip {zip_path}: {e}")
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def send_input(self, connection_id, tensor_input: np.ndarray):
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"""Pre-process (if needed) and send frame to MemryX input queue"""
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if tensor_input is None:
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raise ValueError("[send_input] No image data provided for inference")
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if self.memx_model_type == ModelTypeEnum.yolonas:
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if tensor_input.ndim == 4 and tensor_input.shape[1:] == (320, 320, 3):
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logger.debug("Transposing tensor from NHWC to NCHW for YOLO-NAS")
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tensor_input = np.transpose(
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tensor_input, (0, 3, 1, 2)
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) # (1, H, W, C) → (1, C, H, W)
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tensor_input = tensor_input.astype(np.float32)
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tensor_input /= 255
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if self.memx_model_type == ModelTypeEnum.yolox:
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# Remove batch dim → (3, 640, 640)
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tensor_input = tensor_input.squeeze(0)
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# Convert CHW to HWC for OpenCV
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tensor_input = np.transpose(tensor_input, (1, 2, 0)) # (640, 640, 3)
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padded_img = np.ones((640, 640, 3), dtype=np.uint8) * 114
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scale = min(
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640 / float(tensor_input.shape[0]), 640 / float(tensor_input.shape[1])
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)
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sx, sy = (
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int(tensor_input.shape[1] * scale),
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int(tensor_input.shape[0] * scale),
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)
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resized_img = cv2.resize(
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tensor_input, (sx, sy), interpolation=cv2.INTER_LINEAR
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)
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padded_img[:sy, :sx] = resized_img.astype(np.uint8)
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# Step 4: Slice the padded image into 4 quadrants and concatenate them into 12 channels
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x0 = padded_img[0::2, 0::2, :] # Top-left
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x1 = padded_img[1::2, 0::2, :] # Bottom-left
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x2 = padded_img[0::2, 1::2, :] # Top-right
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x3 = padded_img[1::2, 1::2, :] # Bottom-right
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# Step 5: Concatenate along the channel dimension (axis 2)
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concatenated_img = np.concatenate([x0, x1, x2, x3], axis=2)
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tensor_input = concatenated_img.astype(np.float32)
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# Convert to CHW format (12, 320, 320)
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tensor_input = np.transpose(tensor_input, (2, 0, 1))
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# Add batch dimension → (1, 12, 320, 320)
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tensor_input = np.expand_dims(tensor_input, axis=0)
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# Send frame to MemryX for processing
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self.capture_queue.put(tensor_input)
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self.capture_id_queue.put(connection_id)
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def process_input(self):
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"""Input callback function: wait for frames in the input queue, preprocess, and send to MX3 (return)"""
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while True:
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try:
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# Wait for a frame from the queue (blocking call)
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frame = self.capture_queue.get(
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block=True
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) # Blocks until data is available
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return frame
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except Exception as e:
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logger.info(f"[process_input] Error processing input: {e}")
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time.sleep(0.1) # Prevent busy waiting in case of error
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def receive_output(self):
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"""Retrieve processed results from MemryX output queue + a copy of the original frame"""
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connection_id = (
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self.capture_id_queue.get()
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) # Get the corresponding connection ID
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detections = self.output_queue.get() # Get detections from MemryX
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return connection_id, detections
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def post_process_yolonas(self, output):
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predictions = output[0]
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detections = np.zeros((20, 6), np.float32)
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for i, prediction in enumerate(predictions):
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if i == 20:
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break
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(_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
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if class_id < 0:
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break
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detections[i] = [
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class_id,
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confidence,
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y_min / self.memx_model_height,
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x_min / self.memx_model_width,
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y_max / self.memx_model_height,
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x_max / self.memx_model_width,
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]
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# Return the list of final detections
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self.output_queue.put(detections)
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def process_yolo(self, class_id, conf, pos):
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"""
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Takes in class ID, confidence score, and array of [x, y, w, h] that describes detection position,
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returns an array that's easily passable back to Frigate.
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"""
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return [
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class_id, # class ID
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conf, # confidence score
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(pos[1] - (pos[3] / 2)) / self.memx_model_height, # y_min
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(pos[0] - (pos[2] / 2)) / self.memx_model_width, # x_min
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(pos[1] + (pos[3] / 2)) / self.memx_model_height, # y_max
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(pos[0] + (pos[2] / 2)) / self.memx_model_width, # x_max
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]
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def set_strides_grids(self):
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grids = []
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expanded_strides = []
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strides = [8, 16, 32]
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hsize_list = [self.memx_model_height // stride for stride in strides]
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wsize_list = [self.memx_model_width // stride for stride in strides]
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for hsize, wsize, stride in zip(hsize_list, wsize_list, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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self.grids = np.concatenate(grids, 1)
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self.expanded_strides = np.concatenate(expanded_strides, 1)
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def sigmoid(self, x: np.ndarray) -> np.ndarray:
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return 1 / (1 + np.exp(-x))
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|
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def onnx_concat(self, inputs: list, axis: int) -> np.ndarray:
|
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# Ensure all inputs are numpy arrays
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if not all(isinstance(x, np.ndarray) for x in inputs):
|
|
raise TypeError("All inputs must be numpy arrays.")
|
|
|
|
# Ensure shapes match on non-concat axes
|
|
ref_shape = list(inputs[0].shape)
|
|
for i, tensor in enumerate(inputs[1:], start=1):
|
|
for ax in range(len(ref_shape)):
|
|
if ax == axis:
|
|
continue
|
|
if tensor.shape[ax] != ref_shape[ax]:
|
|
raise ValueError(
|
|
f"Shape mismatch at axis {ax} between input[0] and input[{i}]"
|
|
)
|
|
|
|
return np.concatenate(inputs, axis=axis)
|
|
|
|
def onnx_reshape(self, data: np.ndarray, shape: np.ndarray) -> np.ndarray:
|
|
# Ensure shape is a 1D array of integers
|
|
target_shape = shape.astype(int).tolist()
|
|
|
|
# Use NumPy reshape with dynamic handling of -1
|
|
reshaped = np.reshape(data, target_shape)
|
|
|
|
return reshaped
|
|
|
|
def post_process_yolox(self, output):
|
|
output_785 = output[0] # 785
|
|
output_794 = output[1] # 794
|
|
output_795 = output[2] # 795
|
|
output_811 = output[3] # 811
|
|
output_820 = output[4] # 820
|
|
output_821 = output[5] # 821
|
|
output_837 = output[6] # 837
|
|
output_846 = output[7] # 846
|
|
output_847 = output[8] # 847
|
|
|
|
output_795 = self.sigmoid(output_795)
|
|
output_785 = self.sigmoid(output_785)
|
|
output_821 = self.sigmoid(output_821)
|
|
output_811 = self.sigmoid(output_811)
|
|
output_847 = self.sigmoid(output_847)
|
|
output_837 = self.sigmoid(output_837)
|
|
|
|
concat_1 = self.onnx_concat([output_794, output_795, output_785], axis=1)
|
|
concat_2 = self.onnx_concat([output_820, output_821, output_811], axis=1)
|
|
concat_3 = self.onnx_concat([output_846, output_847, output_837], axis=1)
|
|
|
|
shape = np.array([1, 85, -1], dtype=np.int64)
|
|
|
|
reshape_1 = self.onnx_reshape(concat_1, shape)
|
|
reshape_2 = self.onnx_reshape(concat_2, shape)
|
|
reshape_3 = self.onnx_reshape(concat_3, shape)
|
|
|
|
concat_out = self.onnx_concat([reshape_1, reshape_2, reshape_3], axis=2)
|
|
|
|
output = concat_out.transpose(0, 2, 1) # 1, 840, 85
|
|
|
|
self.num_classes = output.shape[2] - 5
|
|
|
|
# [x, y, h, w, box_score, class_no_1, ..., class_no_80],
|
|
results = output
|
|
|
|
results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides
|
|
results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides
|
|
image_pred = results[0, ...]
|
|
|
|
class_conf = np.max(
|
|
image_pred[:, 5 : 5 + self.num_classes], axis=1, keepdims=True
|
|
)
|
|
class_pred = np.argmax(image_pred[:, 5 : 5 + self.num_classes], axis=1)
|
|
class_pred = np.expand_dims(class_pred, axis=1)
|
|
|
|
conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
|
|
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
|
|
detections = np.concatenate((image_pred[:, :5], class_conf, class_pred), axis=1)
|
|
detections = detections[conf_mask]
|
|
|
|
# Sort by class confidence (index 5) and keep top 20 detections
|
|
ordered = detections[detections[:, 5].argsort()[::-1]][:20]
|
|
|
|
# Prepare a final detections array of shape (20, 6)
|
|
final_detections = np.zeros((20, 6), np.float32)
|
|
for i, object_detected in enumerate(ordered):
|
|
final_detections[i] = self.process_yolo(
|
|
object_detected[6], object_detected[5], object_detected[:4]
|
|
)
|
|
|
|
self.output_queue.put(final_detections)
|
|
|
|
def post_process_ssdlite(self, outputs):
|
|
dets = outputs[0].squeeze(0) # Shape: (1, num_dets, 5)
|
|
labels = outputs[1].squeeze(0)
|
|
|
|
detections = []
|
|
|
|
for i in range(dets.shape[0]):
|
|
x_min, y_min, x_max, y_max, confidence = dets[i]
|
|
class_id = int(labels[i]) # Convert label to integer
|
|
|
|
if confidence < 0.45:
|
|
continue # Skip detections below threshold
|
|
|
|
# Convert coordinates to integers
|
|
x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
|
|
|
|
# Append valid detections [class_id, confidence, x, y, width, height]
|
|
detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
|
|
|
|
final_detections = np.zeros((20, 6), np.float32)
|
|
|
|
if len(detections) == 0:
|
|
# logger.info("No detections found.")
|
|
self.output_queue.put(final_detections)
|
|
return
|
|
|
|
# Convert to NumPy array
|
|
detections = np.array(detections, dtype=np.float32)
|
|
|
|
# Apply Non-Maximum Suppression (NMS)
|
|
bboxes = detections[:, 2:6].tolist() # (x_min, y_min, width, height)
|
|
scores = detections[:, 1].tolist() # Confidence scores
|
|
|
|
indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)
|
|
|
|
if len(indices) > 0:
|
|
indices = indices.flatten()[:20] # Keep only the top 20 detections
|
|
selected_detections = detections[indices]
|
|
|
|
# Normalize coordinates AFTER NMS
|
|
for i, det in enumerate(selected_detections):
|
|
class_id, confidence, x_min, y_min, x_max, y_max = det
|
|
|
|
# Normalize coordinates
|
|
x_min /= self.memx_model_width
|
|
y_min /= self.memx_model_height
|
|
x_max /= self.memx_model_width
|
|
y_max /= self.memx_model_height
|
|
|
|
final_detections[i] = [class_id, confidence, y_min, x_min, y_max, x_max]
|
|
|
|
self.output_queue.put(final_detections)
|
|
|
|
def onnx_reshape_with_allowzero(
|
|
self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0
|
|
) -> np.ndarray:
|
|
shape = shape.astype(int)
|
|
input_shape = data.shape
|
|
output_shape = []
|
|
|
|
for i, dim in enumerate(shape):
|
|
if dim == 0 and allowzero == 0:
|
|
output_shape.append(input_shape[i]) # Copy dimension from input
|
|
else:
|
|
output_shape.append(dim)
|
|
|
|
# Now let NumPy infer any -1 if needed
|
|
reshaped = np.reshape(data, output_shape)
|
|
|
|
return reshaped
|
|
|
|
def process_output(self, *outputs):
|
|
"""Output callback function -- receives frames from the MX3 and triggers post-processing"""
|
|
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
|
if not self.memx_post_model:
|
|
conv_out1 = outputs[0]
|
|
conv_out2 = outputs[1]
|
|
conv_out3 = outputs[2]
|
|
conv_out4 = outputs[3]
|
|
conv_out5 = outputs[4]
|
|
conv_out6 = outputs[5]
|
|
|
|
concat_1 = self.onnx_concat([conv_out1, conv_out2], axis=1)
|
|
concat_2 = self.onnx_concat([conv_out3, conv_out4], axis=1)
|
|
concat_3 = self.onnx_concat([conv_out5, conv_out6], axis=1)
|
|
|
|
shape = np.array([1, 144, -1], dtype=np.int64)
|
|
|
|
reshaped_1 = self.onnx_reshape_with_allowzero(
|
|
concat_1, shape, allowzero=0
|
|
)
|
|
reshaped_2 = self.onnx_reshape_with_allowzero(
|
|
concat_2, shape, allowzero=0
|
|
)
|
|
reshaped_3 = self.onnx_reshape_with_allowzero(
|
|
concat_3, shape, allowzero=0
|
|
)
|
|
|
|
concat_4 = self.onnx_concat([reshaped_1, reshaped_2, reshaped_3], 2)
|
|
|
|
axis = 1
|
|
split_sizes = [64, 80]
|
|
|
|
# Calculate indices at which to split
|
|
indices = np.cumsum(split_sizes)[
|
|
:-1
|
|
] # [64] — split before the second chunk
|
|
|
|
# Perform split along axis 1
|
|
split_0, split_1 = np.split(concat_4, indices, axis=axis)
|
|
|
|
num_boxes = 2100 if self.memx_model_height == 320 else 8400
|
|
shape1 = np.array([1, 4, 16, num_boxes])
|
|
reshape_4 = self.onnx_reshape_with_allowzero(
|
|
split_0, shape1, allowzero=0
|
|
)
|
|
|
|
transpose_1 = reshape_4.transpose(0, 2, 1, 3)
|
|
|
|
axis = 1 # As per ONNX softmax node
|
|
|
|
# Subtract max for numerical stability
|
|
x_max = np.max(transpose_1, axis=axis, keepdims=True)
|
|
x_exp = np.exp(transpose_1 - x_max)
|
|
x_sum = np.sum(x_exp, axis=axis, keepdims=True)
|
|
softmax_output = x_exp / x_sum
|
|
|
|
# Weight W from the ONNX initializer (1, 16, 1, 1) with values 0 to 15
|
|
W = np.arange(16, dtype=np.float32).reshape(
|
|
1, 16, 1, 1
|
|
) # (1, 16, 1, 1)
|
|
|
|
# Apply 1x1 convolution: this is a weighted sum over channels
|
|
conv_output = np.sum(
|
|
softmax_output * W, axis=1, keepdims=True
|
|
) # shape: (1, 1, 4, 8400)
|
|
|
|
shape2 = np.array([1, 4, num_boxes])
|
|
reshape_5 = self.onnx_reshape_with_allowzero(
|
|
conv_output, shape2, allowzero=0
|
|
)
|
|
|
|
# ONNX Slice — get first 2 channels: [0:2] along axis 1
|
|
slice_output1 = reshape_5[:, 0:2, :] # Result: (1, 2, 8400)
|
|
|
|
# Slice channels 2 to 4 → axis = 1
|
|
slice_output2 = reshape_5[:, 2:4, :]
|
|
|
|
# Perform Subtraction
|
|
sub_output = self.const_A - slice_output1 # Equivalent to ONNX Sub
|
|
|
|
# Perform the ONNX-style Add
|
|
add_output = self.const_B + slice_output2
|
|
|
|
sub1 = add_output - sub_output
|
|
|
|
add1 = sub_output + add_output
|
|
|
|
div_output = add1 / 2.0
|
|
|
|
concat_5 = self.onnx_concat([div_output, sub1], axis=1)
|
|
|
|
# Expand B to (1, 1, 8400) so it can broadcast across axis=1 (4 channels)
|
|
const_C_expanded = self.const_C[:, np.newaxis, :] # Shape: (1, 1, 8400)
|
|
|
|
# Perform ONNX-style element-wise multiplication
|
|
mul_output = concat_5 * const_C_expanded # Result: (1, 4, 8400)
|
|
|
|
sigmoid_output = self.sigmoid(split_1)
|
|
outputs = self.onnx_concat([mul_output, sigmoid_output], axis=1)
|
|
|
|
final_detections = post_process_yolo(
|
|
outputs, self.memx_model_width, self.memx_model_height
|
|
)
|
|
self.output_queue.put(final_detections)
|
|
|
|
elif self.memx_model_type == ModelTypeEnum.yolonas:
|
|
return self.post_process_yolonas(outputs)
|
|
|
|
elif self.memx_model_type == ModelTypeEnum.yolox:
|
|
return self.post_process_yolox(outputs)
|
|
|
|
elif self.memx_model_type == ModelTypeEnum.ssd:
|
|
return self.post_process_ssdlite(outputs)
|
|
|
|
else:
|
|
raise Exception(
|
|
f"{self.memx_model_type} is currently not supported for memryx. See the docs for more info on supported models."
|
|
)
|
|
|
|
def detect_raw(self, tensor_input: np.ndarray):
|
|
"""Removed synchronous detect_raw() function so that we only use async"""
|
|
return 0
|