mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-08-31 13:48:19 +02:00
ruff formatting
This commit is contained in:
Tim
parent
6aef5e80d3
commit
cf429549b9
@ -11,7 +11,9 @@ 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("MemryX SDK is not installed. Install it and set up MIX environment.")
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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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@ -23,17 +25,20 @@ 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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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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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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@ -51,7 +56,7 @@ class MemryXDetector(DetectionApi):
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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.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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self.memx_model_height = detector_config.model.height
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@ -61,38 +66,60 @@ class MemryXDetector(DetectionApi):
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self.cache_dir = "/memryx_models"
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if self.memx_model_type == ModelTypeEnum.yolov9:
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self.model_url = "https://developer.memryx.com/example_files/1p2_frigate/yolov9.zip"
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self.model_url = (
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"https://developer.memryx.com/example_files/1p2_frigate/yolov9.zip"
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)
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elif self.memx_model_type == ModelTypeEnum.yolov8:
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self.model_url = "https://developer.memryx.com/example_files/1p2_frigate/yolov8.zip"
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self.model_url = (
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"https://developer.memryx.com/example_files/1p2_frigate/yolov8.zip"
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)
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if self.memx_model_type in [ModelTypeEnum.yolov8, ModelTypeEnum.yolov9]:
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# Shared constants for both yolov8 and yolov9 post-processing
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self.const_A = np.load("/memryx_models/yolov9/_model_22_Constant_9_output_0.npy")
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self.const_B = np.load("/memryx_models/yolov9/_model_22_Constant_10_output_0.npy")
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self.const_C = np.load("/memryx_models/yolov9/_model_22_Constant_12_output_0.npy")
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self.const_A = np.load(
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"/memryx_models/yolov9/_model_22_Constant_9_output_0.npy"
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)
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self.const_B = np.load(
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"/memryx_models/yolov9/_model_22_Constant_10_output_0.npy"
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)
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self.const_C = np.load(
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"/memryx_models/yolov9/_model_22_Constant_12_output_0.npy"
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)
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elif self.memx_model_type == ModelTypeEnum.yolonas:
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self.model_url = "https://developer.memryx.com/example_files/1p2_frigate/yolo_nas.zip"
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self.model_url = (
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"https://developer.memryx.com/example_files/1p2_frigate/yolo_nas.zip"
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)
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self.expected_post_model = "yolo_nas/yolo_nas_s_post.onnx"
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elif self.memx_model_type == ModelTypeEnum.yolox:
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self.model_url = "https://developer.memryx.com/example_files/1p2_frigate/yolox.zip"
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self.model_url = (
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"https://developer.memryx.com/example_files/1p2_frigate/yolox.zip"
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)
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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_url = "https://developer.memryx.com/example_files/1p2_frigate/ssdlite.zip"
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self.expected_post_model = "ssdlite/SSDlite_MobileNet_v2_320_320_3_onnx_post.onnx"
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self.model_url = (
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"https://developer.memryx.com/example_files/1p2_frigate/ssdlite.zip"
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)
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self.expected_post_model = (
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"ssdlite/SSDlite_MobileNet_v2_320_320_3_onnx_post.onnx"
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)
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self.check_and_prepare_model()
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logger.info(f"Initializing MemryX with model: {self.memx_model_path} on device {self.memx_device_path}")
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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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self.accl = AsyncAccl(self.memx_model_path, mxserver_addr="host.docker.internal")
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self.accl = AsyncAccl(
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self.memx_model_path, mxserver_addr="host.docker.internal"
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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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@ -102,7 +129,9 @@ class MemryXDetector(DetectionApi):
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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(f"Loaded MemryX model from {self.memx_model_path} and {self.memx_post_model}")
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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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@ -117,14 +146,20 @@ class MemryXDetector(DetectionApi):
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logger.info(f"Assigned Model Path: {self.memx_model_path}")
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else:
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post_model_file_path = os.path.join(self.cache_dir, self.expected_post_model)
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post_model_file_path = os.path.join(
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self.cache_dir, self.expected_post_model
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)
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# Check if both post model file exist
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if os.path.isfile(post_model_file_path):
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self.memx_post_model = post_model_file_path
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logger.info(f"Post-processing model found at {post_model_file_path}, skipping download.")
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logger.info(
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f"Post-processing model found at {post_model_file_path}, skipping download."
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)
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else:
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logger.info(f"Model files not found. Downloading from {self.model_url}...")
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logger.info(
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f"Model files not found. Downloading from {self.model_url}..."
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)
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zip_path = os.path.join(self.cache_dir, "memryx_model.zip")
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# Download the ZIP file
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@ -143,30 +178,36 @@ class MemryXDetector(DetectionApi):
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self.memx_post_model = os.path.join(self.cache_dir, file)
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logger.info(f"Assigned Model Path: {self.memx_model_path}")
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logger.info(f"Assigned Post-processing Model Path: {self.memx_post_model}")
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logger.info(
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f"Assigned Post-processing Model Path: {self.memx_post_model}"
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)
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# Cleanup: Remove the ZIP file after extraction
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os.remove(zip_path)
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logger.info("Cleaned up ZIP file after extraction.")
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def send_input(self, connection_id, tensor_input: np.ndarray):
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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.yolox:
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tensor_input = tensor_input.squeeze(0)
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padded_img = np.ones((640, 640, 3),
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dtype=np.uint8) * 114
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padded_img = np.ones((640, 640, 3), dtype=np.uint8) * 114
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scale = min(640 / float(tensor_input.shape[0]),
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640 / float(tensor_input.shape[1]))
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sx,sy = int(tensor_input.shape[1] * scale), int(tensor_input.shape[0] * scale)
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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(tensor_input, (sx,sy), interpolation=cv2.INTER_LINEAR)
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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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@ -176,7 +217,7 @@ class MemryXDetector(DetectionApi):
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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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processed_input = concatenated_img.astype(np.float32)
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processed_input = concatenated_img.astype(np.float32)
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else:
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processed_input = tensor_input.astype(np.float32) / 255.0 # Normalize
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# Assuming original input is always NHWC and MemryX wants HWNC:
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@ -191,8 +232,10 @@ class MemryXDetector(DetectionApi):
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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(block=True) # Blocks until data is available
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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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@ -201,7 +244,9 @@ class MemryXDetector(DetectionApi):
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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 = self.capture_id_queue.get() # Get the corresponding connection ID
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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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@ -216,10 +261,10 @@ class MemryXDetector(DetectionApi):
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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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@ -228,7 +273,7 @@ class MemryXDetector(DetectionApi):
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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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@ -244,9 +289,8 @@ class MemryXDetector(DetectionApi):
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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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@ -266,15 +310,13 @@ class MemryXDetector(DetectionApi):
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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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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):
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raise TypeError("All inputs must be numpy arrays.")
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# Ensure shapes match on non-concat axes
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ref_shape = list(inputs[0].shape)
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for i, tensor in enumerate(inputs[1:], start=1):
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@ -282,12 +324,13 @@ class MemryXDetector(DetectionApi):
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if ax == axis:
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continue
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if tensor.shape[ax] != ref_shape[ax]:
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raise ValueError(f"Shape mismatch at axis {ax} between input[0] and input[{i}]")
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raise ValueError(
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f"Shape mismatch at axis {ax} between input[0] and input[{i}]"
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)
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return np.concatenate(inputs, axis=axis)
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def onnx_reshape(self, data: np.ndarray, shape: np.ndarray) -> np.ndarray:
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# Ensure shape is a 1D array of integers
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target_shape = shape.astype(int).tolist()
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@ -295,23 +338,24 @@ class MemryXDetector(DetectionApi):
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reshaped = np.reshape(data, target_shape)
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return reshaped
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def post_process_yolox(self, output):
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output = [np.expand_dims(tensor, axis=0) for tensor in output] # Shape: (1, H, W, C)
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def post_process_yolox(self, output):
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output = [
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np.expand_dims(tensor, axis=0) for tensor in output
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] # Shape: (1, H, W, C)
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# Move channel axis from 3rd (last) position to 1st position → (1, C, H, W)
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output = [np.transpose(tensor, (0, 3, 1, 2)) for tensor in output]
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output_785 = output[0] # 785
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output_794 = output[1] # 794
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output_795 = output[2] # 795
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output_811 = output[3] # 811
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output_820 = output[4] # 820
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output_821 = output[5] # 821
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output_837 = output[6] # 837
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output_846 = output[7] # 846
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output_847 = output[8] # 847
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output_785 = output[0] # 785
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output_794 = output[1] # 794
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output_795 = output[2] # 795
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output_811 = output[3] # 811
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output_820 = output[4] # 820
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output_821 = output[5] # 821
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output_837 = output[6] # 837
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output_846 = output[7] # 846
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output_847 = output[8] # 847
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output_795 = self.sigmoid(output_795)
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output_785 = self.sigmoid(output_785)
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@ -324,7 +368,7 @@ class MemryXDetector(DetectionApi):
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concat_2 = self.onnx_concat([output_820, output_821, output_811], axis=1)
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concat_3 = self.onnx_concat([output_846, output_847, output_837], axis=1)
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shape = np.array([1, 85, -1], dtype=np.int64)
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shape = np.array([1, 85, -1], dtype=np.int64)
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reshape_1 = self.onnx_reshape(concat_1, shape)
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reshape_2 = self.onnx_reshape(concat_2, shape)
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@ -332,7 +376,7 @@ class MemryXDetector(DetectionApi):
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concat_out = self.onnx_concat([reshape_1, reshape_2, reshape_3], axis=2)
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output = concat_out.transpose(0,2,1) #1, 840, 85
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output = concat_out.transpose(0, 2, 1) # 1, 840, 85
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self.num_classes = output.shape[2] - 5
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@ -343,8 +387,10 @@ class MemryXDetector(DetectionApi):
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results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides
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image_pred = results[0, ...]
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class_conf = np.max(image_pred[:, 5:5 + self.num_classes], axis=1, keepdims=True)
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class_pred = np.argmax(image_pred[:, 5:5 + self.num_classes], axis=1)
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class_conf = np.max(
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image_pred[:, 5 : 5 + self.num_classes], axis=1, keepdims=True
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)
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class_pred = np.argmax(image_pred[:, 5 : 5 + self.num_classes], axis=1)
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class_pred = np.expand_dims(class_pred, axis=1)
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conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
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@ -364,7 +410,6 @@ class MemryXDetector(DetectionApi):
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self.output_queue.put(final_detections)
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def post_process_ssdlite(self, outputs):
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dets = outputs[0].squeeze(0) # Shape: (1, num_dets, 5)
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labels = outputs[1].squeeze(0)
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@ -414,12 +459,13 @@ class MemryXDetector(DetectionApi):
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x_max /= self.memx_model_width
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y_max /= self.memx_model_height
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final_detections[i] = [class_id, confidence, y_min, x_min, y_max, x_max]
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final_detections[i] = [class_id, confidence, y_min, x_min, y_max, x_max]
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self.output_queue.put(final_detections)
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def onnx_reshape_with_allowzero(self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0) -> np.ndarray:
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def onnx_reshape_with_allowzero(
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self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0
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) -> np.ndarray:
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shape = shape.astype(int)
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input_shape = data.shape
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output_shape = []
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@ -436,9 +482,11 @@ class MemryXDetector(DetectionApi):
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return reshaped
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def process_output(self, *outputs):
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"""Output callback function -- receives frames from the MX3 and triggers post-processing"""
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"""Output callback function -- receives frames from the MX3 and triggers post-processing"""
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if self.memx_model_type in [ModelTypeEnum.yolov8, ModelTypeEnum.yolov9]:
|
||||
outputs = [np.expand_dims(tensor, axis=0) for tensor in outputs] # Shape: (1, H, W, C)
|
||||
outputs = [
|
||||
np.expand_dims(tensor, axis=0) for tensor in outputs
|
||||
] # Shape: (1, H, W, C)
|
||||
|
||||
# Move channel axis from 3rd (last) position to 1st position → (1, C, H, W)
|
||||
outputs = [np.transpose(tensor, (0, 3, 1, 2)) for tensor in outputs]
|
||||
@ -466,15 +514,17 @@ class MemryXDetector(DetectionApi):
|
||||
split_sizes = [64, 80]
|
||||
|
||||
# Calculate indices at which to split
|
||||
indices = np.cumsum(split_sizes)[:-1] # [64] — split before the second chunk
|
||||
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)
|
||||
|
||||
shape1 = np.array([1,4,16,8400])
|
||||
shape1 = np.array([1, 4, 16, 8400])
|
||||
reshape_4 = self.onnx_reshape_with_allowzero(split_0, shape1, allowzero=0)
|
||||
|
||||
transpose_1 = reshape_4.transpose(0,2,1,3)
|
||||
transpose_1 = reshape_4.transpose(0, 2, 1, 3)
|
||||
|
||||
axis = 1 # As per ONNX softmax node
|
||||
|
||||
@ -488,10 +538,14 @@ class MemryXDetector(DetectionApi):
|
||||
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)
|
||||
conv_output = np.sum(
|
||||
softmax_output * W, axis=1, keepdims=True
|
||||
) # shape: (1, 1, 4, 8400)
|
||||
|
||||
shape2 = np.array([1,4,8400])
|
||||
reshape_5 = self.onnx_reshape_with_allowzero(conv_output, shape2, allowzero=0)
|
||||
shape2 = np.array([1, 4, 8400])
|
||||
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)
|
||||
@ -511,7 +565,7 @@ class MemryXDetector(DetectionApi):
|
||||
|
||||
div_output = add1 / 2.0
|
||||
|
||||
concat_5 = self.onnx_concat([div_output, sub1], axis=1)
|
||||
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)
|
||||
@ -522,23 +576,25 @@ class MemryXDetector(DetectionApi):
|
||||
sigmoid_output = self.sigmoid(split_1)
|
||||
outputs = self.onnx_concat([mul_output, sigmoid_output], axis=1)
|
||||
|
||||
final_detections = post_process_yolov9(outputs, self.memx_model_width, self.memx_model_height)
|
||||
final_detections = post_process_yolov9(
|
||||
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 """
|
||||
"""Removed synchronous detect_raw() function so that we only use async"""
|
||||
return 0
|
||||
|
||||
@ -139,7 +139,7 @@ def run_detector(
|
||||
avg_speed.value = (avg_speed.value * 9 + duration) / 10
|
||||
|
||||
logger.info("Exited detection process...")
|
||||
return self.detect_api.detect_raw(tensor_input=tensor_input)
|
||||
return self.detect_api.detect_raw(tensor_input=tensor_input)
|
||||
|
||||
|
||||
def async_run_detector(
|
||||
@ -194,7 +194,7 @@ def async_run_detector(
|
||||
logger.warning(f"Failed to get frame {connection_id} from SHM")
|
||||
continue
|
||||
|
||||
#send input to Accelator
|
||||
# send input to Accelator
|
||||
start.value = datetime.datetime.now().timestamp()
|
||||
object_detector.detect_api.send_input(connection_id, input_frame)
|
||||
|
||||
@ -231,7 +231,6 @@ def async_run_detector(
|
||||
logger.info("Exited async detection process...")
|
||||
|
||||
|
||||
|
||||
class ObjectDetectProcess:
|
||||
def __init__(
|
||||
self,
|
||||
@ -266,7 +265,7 @@ class ObjectDetectProcess:
|
||||
self.detection_start.value = 0.0
|
||||
if (self.detect_process is not None) and self.detect_process.is_alive():
|
||||
self.stop()
|
||||
if (self.detector_config.type == 'memryx'):
|
||||
if self.detector_config.type == "memryx":
|
||||
# MemryX requires asynchronous detection handling using async_run_detector
|
||||
self.detect_process = util.Process(
|
||||
target=async_run_detector,
|
||||
@ -292,7 +291,7 @@ class ObjectDetectProcess:
|
||||
self.detection_start,
|
||||
self.detector_config,
|
||||
),
|
||||
)
|
||||
)
|
||||
self.detect_process.daemon = True
|
||||
self.detect_process.start()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user