Remove rocm detector (#16913)

* Remove rocm detector plugin

* Update docs to recommend using onnx for rocm

* Formatting

docs/docs/configuration/object_detectors.md

@@ -49,7 +49,7 @@ This does not affect using hardware for accelerating other tasks such as [semant
 
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
 
 ## Edge TPU Detector
 
@@ -367,7 +367,7 @@ model:
 
 ### Setup
 
-The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
+Support for AMD GPUs is provided using the [ONNX detector](#ONNX). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
 
 ### Docker settings for GPU access
 
@@ -446,29 +446,9 @@ $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/
 
 ### Supported Models
 
-There is no default model provided, the following formats are supported:
+See [ONNX supported models](#supported-models) for supported models, there are some caveats:
-
+- D-FINE models are not supported
-#### YOLO-NAS
+- YOLO-NAS models are known to not run well on integrated GPUs
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  rocm:
-    type: rocm
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  path: /config/yolo_nas_s.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## ONNX
 

docs/docs/plus/index.md

@@ -28,11 +28,11 @@ Not all model types are supported by all detectors, so it's important to choose
 
 ## Supported detector types
 
-Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), and ROCm (`rocm`) detectors.
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), and ONNX (`onnx`) detectors.
 
 :::warning
 
-Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 and later.
+Using Frigate+ models with `onnx` is only available with Frigate 0.15 and later.
 
 :::
 
@@ -42,7 +42,7 @@ Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15
 | [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector)                                             | `edgetpu`                 | `mobiledet`            |
 | [Intel](/configuration/object_detectors.md#openvino-detector)                                                                | `openvino`                | `yolonas`              |
 | [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\*                   | `onnx`                    | `yolonas`              |
-| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `rocm`                    | `yolonas`              |
+| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `onnx`                    | `yolonas`              |
 
 _\* Requires Frigate 0.15_
 

frigate/detectors/plugins/rocm.py

@@ -1,170 +0,0 @@
-import ctypes
-import logging
-import os
-import subprocess
-import sys
-
-import cv2
-import numpy as np
-from pydantic import Field
-from typing_extensions import Literal
-
-from frigate.const import MODEL_CACHE_DIR
-from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import (
-    BaseDetectorConfig,
-    ModelTypeEnum,
-    PixelFormatEnum,
-)
-
-logger = logging.getLogger(__name__)
-
-DETECTOR_KEY = "rocm"
-
-
-def detect_gfx_version():
-    return subprocess.getoutput(
-        "unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo | grep gfx |head -1|awk '{print $2}'"
-    )
-
-
-def auto_override_gfx_version():
-    # If environment variable already in place, do not override
-    gfx_version = detect_gfx_version()
-    old_override = os.getenv("HSA_OVERRIDE_GFX_VERSION")
-    if old_override not in (None, ""):
-        logger.warning(
-            f"AMD/ROCm: detected {gfx_version} but HSA_OVERRIDE_GFX_VERSION already present ({old_override}), not overriding!"
-        )
-        return old_override
-    mapping = {
-        "gfx90c": "9.0.0",
-        "gfx1031": "10.3.0",
-        "gfx1103": "11.0.0",
-    }
-    override = mapping.get(gfx_version)
-    if override is not None:
-        logger.warning(
-            f"AMD/ROCm: detected {gfx_version}, overriding HSA_OVERRIDE_GFX_VERSION={override}"
-        )
-        os.putenv("HSA_OVERRIDE_GFX_VERSION", override)
-        return override
-    return ""
-
-
-class ROCmDetectorConfig(BaseDetectorConfig):
-    type: Literal[DETECTOR_KEY]
-    conserve_cpu: bool = Field(
-        default=True,
-        title="Conserve CPU at the expense of latency (and reduced max throughput)",
-    )
-    auto_override_gfx: bool = Field(
-        default=True, title="Automatically detect and override gfx version"
-    )
-
-
-class ROCmDetector(DetectionApi):
-    type_key = DETECTOR_KEY
-
-    def __init__(self, detector_config: ROCmDetectorConfig):
-        if detector_config.auto_override_gfx:
-            auto_override_gfx_version()
-
-        try:
-            sys.path.append("/opt/rocm/lib")
-            import migraphx
-
-            logger.info("AMD/ROCm: loaded migraphx module")
-        except ModuleNotFoundError:
-            logger.error("AMD/ROCm: module loading failed, missing ROCm environment?")
-            raise
-
-        if detector_config.conserve_cpu:
-            logger.info("AMD/ROCm: switching HIP to blocking mode to conserve CPU")
-            ctypes.CDLL("/opt/rocm/lib/libamdhip64.so").hipSetDeviceFlags(4)
-
-        self.h = detector_config.model.height
-        self.w = detector_config.model.width
-        self.rocm_model_type = detector_config.model.model_type
-        self.rocm_model_px = detector_config.model.input_pixel_format
-        path = detector_config.model.path
-
-        mxr_path = os.path.splitext(path)[0] + ".mxr"
-        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:
-            logger.info(f"AMD/ROCm: loading model from {path}")
-
-            if (
-                path.endswith(".tf")
-                or path.endswith(".tf2")
-                or path.endswith(".tflite")
-            ):
-                # untested
-                self.model = migraphx.parse_tf(path)
-            else:
-                self.model = migraphx.parse_onnx(path)
-
-            logger.info("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(os.path.join(MODEL_CACHE_DIR, "rocm"), exist_ok=True)
-            migraphx.save(self.model, mxr_path)
-
-        logger.info("AMD/ROCm: model loaded")
-
-    def detect_raw(self, tensor_input):
-        model_input_name = self.model.get_parameter_names()[0]
-        model_input_shape = tuple(
-            self.model.get_parameter_shapes()[model_input_name].lens()
-        )
-
-        tensor_input = cv2.dnn.blobFromImage(
-            tensor_input[0],
-            1.0,
-            (model_input_shape[3], model_input_shape[2]),
-            None,
-            swapRB=self.rocm_model_px == PixelFormatEnum.bgr,
-        ).astype(np.uint8)
-
-        detector_result = self.model.run({model_input_name: tensor_input})[0]
-        addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
-
-        tensor_output = np.ctypeslib.as_array(
-            addr, shape=detector_result.get_shape().lens()
-        )
-
-        if self.rocm_model_type == ModelTypeEnum.yolonas:
-            predictions = tensor_output
-
-            detections = np.zeros((20, 6), np.float32)
-
-            for i, prediction in enumerate(predictions):
-                if i == 20:
-                    break
-                (_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
-                # when running in GPU mode, empty predictions in the output have class_id of -1
-                if class_id < 0:
-                    break
-                detections[i] = [
-                    class_id,
-                    confidence,
-                    y_min / self.h,
-                    x_min / self.w,
-                    y_max / self.h,
-                    x_max / self.w,
-                ]
-            return detections
-        else:
-            raise Exception(
-                f"{self.rocm_model_type} is currently not supported for rocm. See the docs for more info on supported models."
-            )

frigate/object_detection.py

@@ -17,7 +17,6 @@ from frigate.detectors.detector_config import (
     InputDTypeEnum,
     InputTensorEnum,
 )
-from frigate.detectors.plugins.rocm import DETECTOR_KEY as ROCM_DETECTOR_KEY
 from frigate.util.builtin import EventsPerSecond, load_labels
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.services import listen
@@ -52,13 +51,7 @@ class LocalObjectDetector(ObjectDetector):
             self.labels = load_labels(labels)
 
         if detector_config:
-            if detector_config.type == ROCM_DETECTOR_KEY:
+            self.input_transform = tensor_transform(detector_config.model.input_tensor)
-                # ROCm requires NHWC as input
-                self.input_transform = None
-            else:
-                self.input_transform = tensor_transform(
-                    detector_config.model.input_tensor
-                )
 
             self.dtype = detector_config.model.input_dtype
         else: