mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
116 lines
5.1 KiB
Python
116 lines
5.1 KiB
Python
import logging
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import sys
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import os
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import numpy as np
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import ctypes
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from pydantic import Field
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from typing_extensions import Literal
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import glob
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import subprocess
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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import frigate.detectors.yolo_utils as yolo_utils
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "rocm"
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def detect_gfx_version():
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return subprocess.getoutput("unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo | grep gfx |head -1|awk '{print $2}'")
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def auto_override_gfx_version():
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# If environment varialbe already in place, do not override
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gfx_version = detect_gfx_version()
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old_override = os.getenv('HSA_OVERRIDE_GFX_VERSION')
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if old_override not in (None, ''):
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logger.warning(f"AMD/ROCm: detected {gfx_version} but HSA_OVERRIDE_GFX_VERSION already present ({old_override}), not overriding!")
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return old_override
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mapping = {
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'gfx90c': '9.0.0',
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'gfx1031': '10.3.0',
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'gfx1103': '11.0.0',
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}
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override = mapping.get(gfx_version)
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if override is not None:
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logger.warning(f"AMD/ROCm: detected {gfx_version}, overriding HSA_OVERRIDE_GFX_VERSION={override}")
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os.putenv('HSA_OVERRIDE_GFX_VERSION', override)
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return override
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return ''
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class ROCmDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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conserve_cpu: bool = Field(default=True, title="Conserve CPU at the expense of latency (and reduced max throughput)")
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auto_override_gfx: bool = Field(default=True, title="Automatically detect and override gfx version")
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class ROCmDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: ROCmDetectorConfig):
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if detector_config.auto_override_gfx:
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auto_override_gfx_version()
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try:
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sys.path.append("/opt/rocm/lib")
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import migraphx
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logger.info(f"AMD/ROCm: loaded migraphx module")
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except ModuleNotFoundError:
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logger.error(
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"AMD/ROCm: module loading failed, missing ROCm environment?"
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)
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raise
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if detector_config.conserve_cpu:
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logger.info(f"AMD/ROCm: switching HIP to blocking mode to conserve CPU")
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ctypes.CDLL('/opt/rocm/lib/libamdhip64.so').hipSetDeviceFlags(4)
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assert detector_config.model.model_type == 'yolov8', "AMD/ROCm: detector_config.model.model_type: only yolov8 supported"
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assert detector_config.model.input_tensor == 'nhwc', "AMD/ROCm: detector_config.model.input_tensor: only nhwc supported"
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if detector_config.model.input_pixel_format != 'rgb':
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logger.warn("AMD/ROCm: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!")
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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("/config/model_cache/yolov8/*.onnx")) + " and " + ', '.join(glob.glob("/config/model_cache/yolov8/*_labels.txt"))
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path = detector_config.model.path
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mxr_path = os.path.splitext(path)[0] + '.mxr'
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if path.endswith('.mxr'):
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logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
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self.model = migraphx.load(mxr_path)
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elif os.path.exists(mxr_path):
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logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
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self.model = migraphx.load(mxr_path)
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else:
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logger.info(f"AMD/ROCm: loading model from {path}")
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if path.endswith('.onnx'):
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self.model = migraphx.parse_onnx(path)
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elif path.endswith('.tf') or path.endswith('.tf2') or path.endswith('.tflite'):
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# untested
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self.model = migraphx.parse_tf(path)
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else:
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raise Exception(f"AMD/ROCm: unkown model format {path}")
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logger.info(f"AMD/ROCm: compiling the model")
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self.model.compile(migraphx.get_target('gpu'), offload_copy=True, fast_math=True)
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logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}")
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os.makedirs("/config/model_cache/rocm", exist_ok=True)
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migraphx.save(self.model, mxr_path)
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logger.info(f"AMD/ROCm: model loaded")
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self.class_aggregation = yolo_utils.generate_class_aggregation_from_config(detector_config)
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def detect_raw(self, tensor_input):
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model_input_name = self.model.get_parameter_names()[0];
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model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens());
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tensor_input = yolo_utils.preprocess(tensor_input, model_input_shape, np.float32)
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detector_result = self.model.run({model_input_name: tensor_input})[0]
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addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
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tensor_output = np.ctypeslib.as_array(addr, shape=detector_result.get_shape().lens())
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return yolo_utils.yolov8_postprocess(model_input_shape, tensor_output, class_aggregation = self.class_aggregation)
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