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Include mingraphx type for ROCm support

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This commit is contained in:
Gary Mathews 2025-12-27 20:58:41 -08:00
parent 5fdb56a106
commit 2e86164b01
3 changed files with 294 additions and 1 deletions
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25
frigate/detectors/detector_utils.py
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@ -1,5 +1,6 @@
import logging
import os
import subprocess
import numpy as np
@ -72,3 +73,27 @@ def tflite_load_delegate_interpreter(
)
raise
def detect_amd_gfx_id():
return subprocess.getoutput("unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo 2>/dev/null | grep gfx | head -1 | awk '{print $2}'")
def apply_amd_compatibility_env_vars():
gfx_id = detect_amd_gfx_id()
if not gfx_id:
return
logger.info(f"Setting AMD environment variables for {gfx_id} compatibility...")
configs = {
("gfx902", "gfx909", "gfx90c"): {
"HSA_ENABLE_SDMA": "0", # Disable System Direct Memory Access for APU compatibility
"HSA_OVERRIDE_GFX_VERSION": "9.0.0", # Force compatible GFX version
"MIGRAPHX_DISABLE_MIOPEN_FUSION": "1", # Disable unsupported fusion optimization
}
}
for gfx_ids, vars in configs.items():
if gfx_id in gfx_ids:
for var, value in vars.items():
if var not in os.environ:
os.environ[var] = value
logger.info(f" - \"{var}={value}\"")

263
frigate/detectors/plugins/migraphx.py Normal file
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@ -0,0 +1,263 @@
import logging
import ctypes
import sys
from pathlib import Path
from typing import Literal
import numpy as np
from pydantic import Field
from frigate.const import MODEL_CACHE_DIR
from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.detectors.detector_utils import apply_amd_compatibility_env_vars
logger = logging.getLogger(__name__)
DETECTOR_KEY = "migraphx"
class migraphxDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY]
device: str = Field(default="gpu", title="Device Type (gpu or cpu)")
conserve_cpu: bool = Field(default=True, title="Conserve CPU at the expense of latency")
fast_math: bool = Field(default=True, title="Optimize math functions to use faster approximate versions")
exhaustive_tune: bool = Field(default=False, title="Use exhaustive search to find the fastest generated kernels")
class migraphxDetector(DetectionApi):
type_key = DETECTOR_KEY
def __init__(self, detector_config: migraphxDetectorConfig):
super().__init__(detector_config)
apply_amd_compatibility_env_vars()
try:
sys.path.append("/opt/rocm/lib")
import migraphx
logger.info(f"migraphx: loaded migraphx module")
except ModuleNotFoundError:
logger.error("migraphx: module loading failed, missing migraphx")
raise
assert detector_config.model.path is not None, "No model.path configured, please configure model.path"
assert detector_config.model.labelmap_path is not None, "No model.labelmap_path configured, please configure model.labelmap_path"
device = detector_config.device.lower()
assert device in ["gpu", "cpu"], "Invalid device set, must be gpu (default) or cpu"
try:
if device == "gpu":
if detector_config.conserve_cpu:
logger.info(f"migraphx: enabling hipDeviceScheduleYield to forcefully conserve CPU (conserve_cpu=true)")
ctypes.CDLL('/opt/rocm/lib/libamdhip64.so').hipSetDeviceFlags(4)
else:
# Default to hipDeviceScheduleAuto
ctypes.CDLL('/opt/rocm/lib/libamdhip64.so').hipSetDeviceFlags(0)
except Exception as e:
logger.warning(f"migraphx: could not set hipSetDeviceFlags: {e}")
cache_dir = Path(MODEL_CACHE_DIR) / "migraphx"
cache_dir.mkdir(parents=True, exist_ok=True)
path = Path(detector_config.model.path)
filename = path.stem + ('_cpu' if device == 'cpu' else ('_tune' if detector_config.exhaustive_tune else ''))
mxr_cache_path = cache_dir / f"{filename}.mxr"
mxr_path = path.parent / f"{filename}.mxr"
if mxr_cache_path.exists():
logger.info(f"migraphx: loading compiled model from cache {mxr_cache_path}")
self.model = migraphx.load(str(mxr_cache_path))
elif mxr_path.exists():
logger.info(f"migraphx: loading compiled model from {mxr_path}")
self.model = migraphx.load(str(mxr_path))
else:
logger.info(f"migraphx: loading model from {path}")
if path.suffix == '.onnx':
self.model = migraphx.parse_onnx(str(path))
else:
raise Exception(f"migraphx: unknown model format {path}")
self.model_output_shapes = self.model.get_output_shapes()
self.model_is_nms = self.model_output_shapes[0].lens()[2] <= 7
assert self.model_is_nms is False, "migraphx does not currently support NMS models"
logger.info(f"migraphx: compiling the model... (fast_math: {detector_config.fast_math}, exhaustive_tune: {detector_config.exhaustive_tune})")
self.model.compile(
migraphx.get_target(device),
offload_copy=True,
fast_math=detector_config.fast_math,
exhaustive_tune=detector_config.exhaustive_tune
)
logger.info(f"migraphx: saving compiled model into cache {mxr_cache_path}")
migraphx.save(self.model, str(mxr_cache_path))
self.model_param_name = self.model.get_parameter_names()[0]
self.model_input_shape_obj = self.model.get_parameter_shapes()[self.model_param_name]
self.model_input_shape = tuple(self.model_input_shape_obj.lens())
self.model_input_arg = migraphx.generate_argument(self.model_input_shape_obj)
self.model_input_array = np.frombuffer(self.model_input_arg, dtype=np.float32).reshape(self.model_input_shape)
logger.info(f"migraphx: model loaded (input: {self.model_input_shape})")
def preprocess(self, tensor_input):
# Ensure we have a 4D tensor
if tensor_input.ndim == 3:
tensor_input = np.expand_dims(tensor_input, axis=0)
target_n, target_c, target_h, target_w = self.model_input_shape
# Ensure input shape is NCHW
if tensor_input.shape[1] == target_c:
# Already (N, C, H, W), do nothing
pass
elif tensor_input.shape[3] == target_c:
# From (N, H, W, C) to (N, C, H, W)
tensor_input = tensor_input.transpose(0, 3, 1, 2)
elif tensor_input.shape[2] == target_c:
# From (N, H, C, W) to (N, C, H, W)
tensor_input = tensor_input.transpose(0, 2, 1, 3)
np.copyto(self.model_input_array, tensor_input)
def detect_raw(self, tensor_input):
self.preprocess(tensor_input)
outputs = self.model.run({self.model_param_name: self.model_input_arg})
tensor_output = np.frombuffer(
outputs[0],
dtype=np.float32
).reshape(outputs[0].get_shape().lens())
return self.optimized_process_yolo(tensor_output)
def optimized_process_yolo(
self,
tensor_output,
confidence_threshold=0.4,
intersection_over_union_threshold=0.4,
top_k=100
):
# Transpose the raw output so each row represents one candidate detection
# Typical YOLO format: [batch, features, candidates] -> [candidates, features]
candidate_detections = tensor_output[0].T
# Extract class probability scores
class_probability_matrix = candidate_detections[:, 4:]
# Identify the highest class score and its index for every candidate box
max_class_scores = np.max(class_probability_matrix, axis=1)
# Create a boolean mask to filter out low-confidence predictions immediately
confidence_mask = max_class_scores > confidence_threshold
# Early exit if no detections meet the minimum confidence requirements
if not np.any(confidence_mask):
return np.zeros((20, 6), dtype=np.float32)
# Filter the detections, scores, and class IDs using the boolean mask
filtered_detections = candidate_detections[confidence_mask]
filtered_scores = max_class_scores[confidence_mask]
# Limit detections to top_k, reducing the set
if len(filtered_scores) > top_k:
partition_idx = np.argpartition(-filtered_scores, top_k)[:top_k]
filtered_detections = filtered_detections[partition_idx]
filtered_scores = filtered_scores[partition_idx]
# Obtain class IDs of reduced set
filtered_class_ids = np.argmax(filtered_detections[:, 4:], axis=1)
# YOLO boxes are typically [center_x, center_y, width, height]
center_coordinates_and_dimensions = filtered_detections[:, :4]
# Pre-calculate an inversion scale to convert pixels to 0.0-1.0 range
# This replaces multiple division operations with a single multiplication later
normalization_scale_vector = np.array([
1.0 / self.width,
1.0 / self.height,
1.0 / self.width,
1.0 / self.height
], dtype=np.float32)
# Calculate half-widths and half-heights to find box corners
half_dimensions = center_coordinates_and_dimensions[:, 2:4] * 0.5
# Initialize a buffer for the corner-format boxes [x1, y1, x2, y2]
corner_format_boxes = np.empty_like(center_coordinates_and_dimensions)
# Calculate Top-Left corners (x1, y1)
corner_format_boxes[:, :2] = (center_coordinates_and_dimensions[:, :2] - half_dimensions)
# Calculate Bottom-Right corners (x2, y2)
corner_format_boxes[:, 2:4] = (center_coordinates_and_dimensions[:, :2] + half_dimensions)
# Transform pixel coordinates into normalized values (0.0 to 1.0)
normalized_boxes = corner_format_boxes * normalization_scale_vector
# Apply Non-Maximum Suppression (NMS) to remove redundant, overlapping boxes
# returns indices of detections to keep
nms_kept_indices = self.optimized_nms(
normalized_boxes,
filtered_scores,
intersection_over_union_threshold
)
# Limit the number of detections to the top 20
number_of_detections_to_save = min(len(nms_kept_indices), 20)
top_detection_indices = nms_kept_indices[:number_of_detections_to_save]
# Pre-allocate a fixed-size result array (20 detections, 6 columns each)
# Column structure: [class_id, confidence, y1, x1, y2, x2]
results = np.zeros((20, 6), dtype=np.float32)
# Bulk assign data into results using the NMS indices
# We explicitly swap X and Y during assignment to meet the [y1, x1, y2, x2] requirement
results[:number_of_detections_to_save, 0] = filtered_class_ids[top_detection_indices] # class_id
results[:number_of_detections_to_save, 1] = filtered_scores[top_detection_indices] # confidence
# Coordinate Mapping
results[:number_of_detections_to_save, 2] = normalized_boxes[top_detection_indices, 1] # y1
results[:number_of_detections_to_save, 3] = normalized_boxes[top_detection_indices, 0] # x1
results[:number_of_detections_to_save, 4] = normalized_boxes[top_detection_indices, 3] # y2
results[:number_of_detections_to_save, 5] = normalized_boxes[top_detection_indices, 2] # x2
return results
def optimized_nms(self, boxes, scores, iou_threshold):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1) * (y2 - y1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
if order.size == 1: break
# Calculate intersection
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
inter = w * h
# IoU = Inter / (Area1 + Area2 - Inter)
ovr = inter / (areas[i] + areas[order[1:]] - inter)
# Filter indices
inds = np.where(ovr <= iou_threshold)[0]
order = order[inds + 1]
return keep

7
frigate/detectors/plugins/onnx.py
View File

@ -5,11 +5,12 @@ from pydantic import Field
from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detection_runners import get_optimized_runner
from frigate.detectors.detector_config import (
BaseDetectorConfig,
ModelTypeEnum,
)
from frigate.detectors.detector_utils import apply_amd_compatibility_env_vars
from frigate.util.model import (
post_process_dfine,
post_process_rfdetr,
@ -33,9 +34,13 @@ class ONNXDetector(DetectionApi):
def __init__(self, detector_config: ONNXDetectorConfig):
super().__init__(detector_config)
apply_amd_compatibility_env_vars()
path = detector_config.model.path
logger.info(f"ONNX: loading {detector_config.model.path}")
from frigate.detectors.detection_runners import get_optimized_runner
self.runner = get_optimized_runner(
path,
detector_config.device,