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fix typos (#9895)
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@ -204,7 +204,7 @@ model:
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### Intel NCS2 VPU and Myriad X Setup
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Intel produces a neural net inference accelleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for accelleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.
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Intel produces a neural net inference acceleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for acceleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.
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```bash
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sudo usermod -a -G users "$(whoami)"
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@ -403,7 +403,7 @@ model: # required
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Explanation for rknn specific options:
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- **core mask** controls which cores of your NPU should be used. This option applies only to SoCs with a multicore NPU (at the time of writing this in only the RK3588/S). The easiest way is to pass the value as a binary number. To do so, use the prefix `0b` and write a `0` to disable a core and a `1` to enable a core, whereas the last digit coresponds to core0, the second last to core1, etc. You also have to use the cores in ascending order (so you can't use core0 and core2; but you can use core0 and core1). Enabling more cores can reduce the inference speed, especially when using bigger models (see section below). Examples:
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- **core mask** controls which cores of your NPU should be used. This option applies only to SoCs with a multicore NPU (at the time of writing this in only the RK3588/S). The easiest way is to pass the value as a binary number. To do so, use the prefix `0b` and write a `0` to disable a core and a `1` to enable a core, whereas the last digit corresponds to core0, the second last to core1, etc. You also have to use the cores in ascending order (so you can't use core0 and core2; but you can use core0 and core1). Enabling more cores can reduce the inference speed, especially when using bigger models (see section below). Examples:
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- `core_mask: 0b000` or just `core_mask: 0` let the NPU decide which cores should be used. Default and recommended value.
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- `core_mask: 0b001` use only core0.
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- `core_mask: 0b011` use core0 and core1.
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@ -609,4 +609,3 @@ Other settings available for the rocm detector
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### Expected performance
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On an AMD Ryzen 3 5400U with integrated GPU (gfx90c) the yolov8n runs in around 9ms per image (about 110 detections per second) and 18ms (55 detections per second) for yolov8s (at 320x320 detector resolution).
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@ -3,7 +3,7 @@ id: ha_network_storage
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title: Home Assistant network storage
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---
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As of Home Asisstant Core 2023.6, Network Mounted Storage is supported for addons.
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As of Home Assistant Core 2023.6, Network Mounted Storage is supported for addons.
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## Setting Up Remote Storage For Frigate
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@ -43,7 +43,7 @@ Accepts the following query string parameters:
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Example parameters:
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- `h=300`: resizes the image to 300 pixes tall
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- `h=300`: resizes the image to 300 pixels tall
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### `GET /api/stats`
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@ -105,10 +105,10 @@ class Rknn(DetectionApi):
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if (config.model.width != 320) or (config.model.height != 320):
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logger.error(
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"Make sure to set the model width and heigth to 320 in your config.yml."
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"Make sure to set the model width and height to 320 in your config.yml."
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)
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raise Exception(
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"Make sure to set the model width and heigth to 320 in your config.yml."
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"Make sure to set the model width and height to 320 in your config.yml."
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)
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if config.model.input_pixel_format != "bgr":
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@ -25,7 +25,7 @@ def detect_gfx_version():
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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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# If environment variable 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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@ -116,7 +116,7 @@ class ROCmDetector(DetectionApi):
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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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raise Exception(f"AMD/ROCm: unknown model format {path}")
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logger.info("AMD/ROCm: compiling the model")
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self.model.compile(
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migraphx.get_target("gpu"), offload_copy=True, fast_math=True
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@ -43,7 +43,7 @@ class TestUserPassMasking(unittest.TestCase):
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self.rtsp_log_message = "Did you mean file:rtsp://user:password@192.168.1.3:554"
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def test_rtsp_in_log_message(self):
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"""Test that the rtsp url in a log message is espaced."""
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"""Test that the rtsp url in a log message is escaped."""
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escaped = clean_camera_user_pass(self.rtsp_log_message)
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print(f"The escaped is {escaped}")
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assert escaped == "Did you mean file:rtsp://*:*@192.168.1.3:554"
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@ -347,7 +347,7 @@ def yuv_to_3_channel_yuv(yuv_frame):
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# flatten the image into array
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yuv_data = yuv_frame.ravel()
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# create a numpy array to hold all the 3 chanel yuv data
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# create a numpy array to hold all the 3 channel yuv data
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all_yuv_data = np.empty((height, width, 3), dtype=np.uint8)
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y_count = height * width
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@ -575,7 +575,7 @@ def intersection_over_union(box_a, box_b):
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# compute the intersection over union by taking the intersection
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# area and dividing it by the sum of prediction + ground-truth
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# areas - the interesection area
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# areas - the intersection area
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iou = inter_area / float(box_a_area + box_b_area - inter_area)
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# return the intersection over union value
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