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detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters

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This commit is contained in:
Indrek Mandre 2024-01-26 10:30:01 +02:00
parent 42f1168898
commit d88accf304
1 changed files with 37 additions and 32 deletions
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69
frigate/detectors/plugins/rocm.py
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@ -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)