From d88accf3043aaf652f6f718dc89a5574599ad02e Mon Sep 17 00:00:00 2001 From: Indrek Mandre Date: Fri, 26 Jan 2024 10:30:01 +0200 Subject: [PATCH] detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters --- frigate/detectors/plugins/rocm.py | 69 +++++++++++++++++-------------- 1 file changed, 37 insertions(+), 32 deletions(-) diff --git a/frigate/detectors/plugins/rocm.py b/frigate/detectors/plugins/rocm.py index a989dfd9c..ad7d1f6b0 100644 --- a/frigate/detectors/plugins/rocm.py +++ b/frigate/detectors/plugins/rocm.py @@ -7,6 +7,7 @@ import ctypes from pydantic import Field from typing_extensions import Literal import glob +import cv2 from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detector_config import BaseDetectorConfig @@ -15,6 +16,25 @@ logger = logging.getLogger(__name__) DETECTOR_KEY = "rocm" +# XXX several detectors run yolov8, this should probably be common code in some utils module +def postprocess_yolov8(model_input_shape, tensor_output, box_count = 20): + model_box_count = tensor_output.shape[2] + model_class_count = tensor_output.shape[1] - 4 + probs = tensor_output[0, 4:, :] + all_ids = np.argmax(probs, axis=0) + all_confidences = np.take(probs.T, model_class_count*np.arange(0, model_box_count) + all_ids) + all_boxes = tensor_output[0, 0:4, :].T + mask = (all_confidences > 0.30) + class_ids = all_ids[mask] + confidences = all_confidences[mask] + cx, cy, w, h = all_boxes[mask].T + scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3] + detections = np.stack((class_ids, confidences, scale_y * (cy - h / 2), scale_x * (cx - w / 2), scale_y * (cy + h / 2), scale_x * (cx + w / 2)), axis=1) + if detections.shape[0] > box_count: + detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]] + detections.resize((box_count, 6)) + return detections + class ROCmDetectorConfig(BaseDetectorConfig): type: Literal[DETECTOR_KEY] @@ -23,7 +43,7 @@ class ROCmDetector(DetectionApi): def __init__(self, detector_config: ROCmDetectorConfig): try: - sys.path.append('/opt/rocm/lib') + sys.path.append("/opt/rocm/lib") import migraphx logger.info(f"AMD/ROCm: loaded migraphx module") @@ -33,11 +53,19 @@ class ROCmDetector(DetectionApi): ) raise + assert detector_config.model.model_type == 'yolov8', "AMD/ROCm: detector_config.model.model_type: only yolov8 supported" + assert detector_config.model.input_tensor == 'nhwc', "AMD/ROCm: detector_config.model.input_tensor: only nhwc supported" + if detector_config.model.input_pixel_format != 'rgb': + logger.warn("AMD/ROCm: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!") + assert detector_config.model.path is not None, "No model.path configured, please configure model.path and model.labelmap_path; some suggestions: " + ', '.join(glob.glob("/*.onnx")) + " and " + ', '.join(glob.glob("/*_labels.txt")) + path = detector_config.model.path - os.makedirs("/config/model_cache/rocm", exist_ok=True) mxr_path = "/config/model_cache/rocm/" + os.path.basename(os.path.splitext(path)[0] + '.mxr') - if os.path.exists(mxr_path): + if path.endswith('.mxr'): + logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}") + self.model = migraphx.load(mxr_path) + elif os.path.exists(mxr_path): logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}") self.model = migraphx.load(mxr_path) else: @@ -45,12 +73,14 @@ class ROCmDetector(DetectionApi): if path.endswith('.onnx'): self.model = migraphx.parse_onnx(path) elif path.endswith('.tf') or path.endswith('.tf2') or path.endswith('.tflite'): + # untested self.model = migraphx.parse_tf(path) else: - raise Exception(f'AMD/ROCm: unkown model format {path}') + raise Exception(f"AMD/ROCm: unkown model format {path}") logger.info(f"AMD/ROCm: compiling the model") self.model.compile(migraphx.get_target('gpu'), offload_copy=True, fast_math=True) logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}") + os.makedirs("/config/model_cache/rocm", exist_ok=True) migraphx.save(self.model, mxr_path) logger.info(f"AMD/ROCm: model loaded") @@ -58,37 +88,12 @@ class ROCmDetector(DetectionApi): model_input_name = self.model.get_parameter_names()[0]; model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens()); - # adapt to nchw/nhwc shape dynamically - if (tensor_input.shape[0], tensor_input.shape[3], tensor_input.shape[1], tensor_input.shape[2]) == model_input_shape: - tensor_input = np.transpose(tensor_input, (0, 3, 1, 2)) - - assert tensor_input.shape == model_input_shape, f"invalid shapes for input ({tensor_input.shape}) and model ({model_input_shape}):" - - tensor_input = (1 / 255.0) * np.ascontiguousarray(tensor_input, dtype=np.float32) + tensor_input = cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False) detector_result = self.model.run({model_input_name: tensor_input})[0] addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float)) - npr = np.ctypeslib.as_array(addr, shape=detector_result.get_shape().lens()) + tensor_output = np.ctypeslib.as_array(addr, shape=detector_result.get_shape().lens()) - model_box_count = npr.shape[2] - model_class_count = npr.shape[1] - 4 - - probs = npr[0, 4:, :] - all_ids = np.argmax(probs, axis=0) - all_confidences = np.take(probs.T, model_class_count*np.arange(0, model_box_count) + all_ids) - all_boxes = npr[0, 0:4, :].T - mask = (all_confidences > 0.25) - class_ids = all_ids[mask] - confidences = all_confidences[mask] - cx, cy, w, h = all_boxes[mask].T - - detections = np.stack((class_ids, confidences, cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2), axis=1) - if detections.shape[0] > 20: - logger.warn(f'Found {detections.shape[0]} boxes, discarding last {detections.shape[0] - 20} entries to limit to 20') - # keep best confidences - detections = detections[detections[:,1].argsort()[::-1]] - detections.resize((20, 6)) - - return detections + return postprocess_yolov8(model_input_shape, tensor_output)