@Frank I want to understand the meaning of GPUIDLECYCLES and GPUTOTALCYCLES in context when we have multiple processes running
let’s say if we have 3 processes running concurrently, and after the inference of each image galcore logs throw out the GPUTOTALCYLCES and GPUIDLECYCLES at the end of the inference.
So, my question is that are these values global with respect to a sample of time slots or are these values with respect to each process?
2 options of the meaning: global result: for 3 concurrent processes and a specific timestamp of 10ms we get the galcore log output → GPUIDLECYCLES & GPUTOTALCYCLES for all processes concurrently per process result: for each process and each image inference we get the galcore log output → GPUIDLECYCLES & GPUTOTALCYCLEDS for each process and per image independent of other images running concurrently
@Frank I am converting a tflite model using the conversion scripts provided by khadas, but I see the following logs. Could you please help me understand, if there is any issue during the conversion of the model.
Also, do we need to do de-quantization after we have done the inference, because after quantization of the model it results the output in int8 format, but I need it in float32 format.
@Frank@numbqq When I run the demo, I am able to get the output, however, I am not able to visualize the image like seen on the demo. Do you have any reason or solution please? I would like to visualize the result of object detection.
@Frank@numbqq
I tried to convert my Yolov3 model to make it compatible with NPU, however, I am not getting the result.
I used aml_npu_app to convert the model.
Can you aslo please tell me if I can do the same with Tengine SDK and aml_npu_nnsdk as I was not successful with aml_npu_app?
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import cv2
Traceback (most recent call last):
File “”, line 1, in
ImportError: /lib/aarch64-linux-gnu/libgtk-3.so.0: undefined symbol: g_mount_operation_set_is_tcrypt_hidden_volume
Hi, I must ask from where you have ordered indoor drone?I recommend is Khadas VIM3. Because of my non-standard neural network implementation, the NPU on the VIM3 is not suitable for me but it may be suitable for you.
NB: The Khadas VIM3 NPU uses floating-point weights and biases. Mine doesn’t.
The case is the solid aluminium variety with dual cooling fans. This provides both low power cooling and relatively high efficiency. This offsets the higher power consumption of the Pi4.