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Published
20 hours ago •
blakeblackshear
blakeblackshear
[Support]: Assistance getting tensorrt running
Describe the problem you are having
Trying to understand the guide to get tensorrt setup,
https://deploy-preview-4055--frigate-docs.netlify.app/configuration/detectors/#nvidia-tensorrt-detector
I have run so far
mkdir trt-models
wget https://raw.githubusercontent.com/blakeblackshear/frigate/docker/tensorrt_models.sh
chmod +x tensorrt_models.sh
docker run --gpus=all --rm -it -v `pwd`/volume2/trtmodels:/tensorrt_models -v `pwd`/tensorrt_models.sh:/tensorrt_models.sh nvcr.io/nvidia/tensorrt:22.07-py3 /tensorrt_models.sh
What I am confused about is how to configure the docker run command. This is what I am currently using
docker run -d --gpus "device=1" --name frigate_tensor --restart=unless-stopped --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 --device /dev/bus/usb:/dev/bus/usb --shm-size=256m -v /volume2/frigate/_data:/media/frigate -v /var/lib/docker/volumes/frigate_config/_data/config.yml:/config/config.yml:ro -v /etc/localtime:/etc/localtime:ro -e FRIGATE_RTSP_PASSWORD='secure' -p 5000:5000 -p 1935:1935 ghcr.io/blakeblackshear/frigate:dev-edbdbb7-tensorrt
Also at the end of generating the models, it spit this out Serialized the TensorRT engine to file: yolov7-tiny-416.trt Is this what I need to set with -e
Version
0.12.0-EDBDBB7
Frigate config file
mqtt:
host: host
port: 1883
topic_prefix: frigate
client_id: frigate
user: liquidmosq
password: secure
birdseye:
enabled: True
width: 1920
height: 1080
quality: 8
mode: objects
cameras:
Front_Garage:
ffmpeg:
output_args:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -ar 44100 -c:a aac
rtmp: -c:v copy -f flv -ar 44100 -c:a aac
inputs:
- path: camera
roles:
- detect
- rtmp
hwaccel_args: preset-nvidia-h264
objects:
track:
- person
- car
- truck
- bicycle
- motorcycle
- dog
- cat
snapshots:
enabled: True
record:
enabled: True
retain:
days: 90
mode: motion
events:
retain:
default: 90
mode: active_objects
motion:
mask:
- 0,151,1280,188,1280,0,0,0
- 680,440,997,720,594,720,388,720,388,451
Back_Alley:
ffmpeg:
output_args:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -ar 44100 -c:a aac
rtmp: -c:v copy -f flv -ar 44100 -c:a aac
inputs:
- path: camera
roles:
- detect
- rtmp
hwaccel_args: preset-nvidia-h264
objects:
track:
- person
- car
- truck
- bicycle
- motorcycle
- dog
- cat
snapshots:
enabled: True
record:
enabled: True
retain:
days: 90
mode: motion
events:
retain:
default: 90
mode: active_objects
motion:
mask:
- 804,720,815,518,733,179,480,0,270,0,114,127,0,253,0,720
- 1109,215,948,186,942,275,1045,420,1186,430,1212,284
- 708,168,1093,165,1182,152,1159,112,599,90
Relevant log output
2023-01-26 23:07:15.301969183 [2023-01-26 23:07:15] frigate.app INFO : Starting Frigate (0.12.0-edbdbb7)
2023-01-26 23:07:15.332469612 [2023-01-26 23:07:15] peewee_migrate INFO : Starting migrations
2023-01-26 23:07:15.361975295 [2023-01-26 23:07:15] peewee_migrate INFO : There is nothing to migrate
2023-01-26 23:07:15.372612370 [2023-01-26 23:07:15] ws4py INFO : Using epoll
2023-01-26 23:07:15.393944948 [2023-01-26 23:07:15] detector.cpu INFO : Starting detection process: 276
2023-01-26 23:07:15.394132605 [2023-01-26 23:07:15] frigate.detectors WARNING : CPU detectors are not recommended and should only be used for testing or for trial purposes.
2023-01-26 23:07:15.395090557 [2023-01-26 23:07:15] frigate.app INFO : Output process started: 278
2023-01-26 23:07:15.402318978 [2023-01-26 23:07:15] frigate.app INFO : Camera processor started for Front_Garage: 280
2023-01-26 23:07:15.406954071 [2023-01-26 23:07:15] ws4py INFO : Using epoll
2023-01-26 23:07:15.414546020 [2023-01-26 23:07:15] frigate.app INFO : Camera processor started for Back_Alley: 283
2023-01-26 23:07:15.414985691 [2023-01-26 23:07:15] frigate.app INFO : Capture process started for Front_Garage: 284
2023-01-26 23:07:15.426444265 [2023-01-26 23:07:15] frigate.app INFO : Capture process started for Back_Alley: 288
2023-01-26 23:07:18.769517201 [2023-01-26 23:07:18] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33414]
2023-01-26 23:07:21.118319538 [2023-01-26 23:07:21] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33496]
2023-01-26 23:07:22.725279126 [2023-01-26 23:07:22] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33414]
2023-01-26 23:07:22.924929596 [2023-01-26 23:07:22] ws4py INFO : Managing websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:42436]
2023-01-26 23:07:22.981325871 [2023-01-26 23:07:22] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33512]
2023-01-26 23:07:35.422142903 [2023-01-26 23:07:35] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33496]
2023-01-26 23:07:35.632975092 [2023-01-26 23:07:35] ws4py INFO : Managing websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:50078]
2023-01-26 23:07:35.707798744 [2023-01-26 23:07:35] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:58304]
2023-01-26 23:08:12.328452823 [2023-01-26 23:08:12] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:58304]
2023-01-26 23:08:13.329555926 [2023-01-26 23:08:13] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:36890]
2023-01-26 23:08:14.507939565 [2023-01-26 23:08:14] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33512]
2023-01-26 23:08:15.385035791 [2023-01-26 23:08:15] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:36892]
2023-01-26 23:09:58.741386143 [2023-01-26 23:09:58] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:36890]
2023-01-26 23:09:58.883393180 [2023-01-26 23:09:58] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:37886]
2023-01-26 23:10:11.242707040 [2023-01-26 23:10:11] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:37886]
2023-01-26 23:10:11.387195875 [2023-01-26 23:10:11] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:53804]
2023-01-26 23:10:23.602505028 [2023-01-26 23:10:23] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:53804]
2023-01-26 23:11:00.699716790 [2023-01-26 23:11:00] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:56522]
2023-01-26 23:11:18.025176141 [2023-01-26 23:11:18] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:56522]
2023-01-26 23:11:18.144025838 [2023-01-26 23:11:18] ws4py INFO : Managing websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33658]
2023-01-26 23:11:46.616205763 [2023-01-26 23:11:46] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:33658]
2023-01-27 01:49:42.745289515 [2023-01-27 01:49:42] ws4py INFO : Terminating websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:42436]
2023-01-27 01:49:42.745298156 [2023-01-27 01:49:42] ws4py INFO : Terminating websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:50078]
2023-01-27 07:37:06.676913878 [2023-01-27 07:37:06] ws4py INFO : Managing websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:55122]
2023-01-27 07:37:07.835753205 [2023-01-27 07:37:07] ws4py INFO : Terminating websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:55122]
2023-01-27 16:16:26.326150316 [2023-01-27 16:16:26] ws4py INFO : Terminating websocket [Local => 127.0.0.1:8082 | Remote => 127.0.0.1:36892]
FFprobe output from your camera
"[\n {\n \"return_code\": 0,\n \"stderr\": {},\n \"stdout\": {\n \"programs\": [],\n \"streams\": [\n {\n \"avg_frame_rate\": \"0/0\",\n \"codec_long_name\": \"AAC (Advanced Audio Coding)\"\n },\n {\n \"avg_frame_rate\": \"25/1\",\n \"codec_long_name\": \"H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10\",\n \"display_aspect_ratio\": \"16:9\",\n \"height\": 1080,\n \"width\": 1920\n }\n ]\n }\n }\n]"
Frigate stats
{"Back_Alley":{"camera_fps":5.1,"capture_pid":288,"detection_enabled":1,"detection_fps":0.1,"ffmpeg_pid":294,"pid":283,"process_fps":5.1,"skipped_fps":0.0},"Front_Garage":{"camera_fps":5.0,"capture_pid":284,"detection_enabled":1,"detection_fps":0.0,"ffmpeg_pid":290,"pid":280,"process_fps":5.0,"skipped_fps":0.0},"cpu_usages":{"%Cpu(s):":{"cpu":"id,","mem":"0.1"},"1":{"cpu":"0.0","mem":"0.0"},"103":{"cpu":"0.0","mem":"0.1"},"122":{"cpu":"0.0","mem":"0.0"},"123":{"cpu":"0.0","mem":"0.1"},"124":{"cpu":"0.0","mem":"0.0"},"125":{"cpu":"0.0","mem":"0.4"},"15":{"cpu":"0.0","mem":"0.0"},"16":{"cpu":"0.0","mem":"0.0"},"24":{"cpu":"0.0","mem":"0.0"},"25":{"cpu":"0.0","mem":"0.0"},"26":{"cpu":"0.0","mem":"0.0"},"269":{"cpu":"0.0","mem":"1.8"},"27":{"cpu":"0.0","mem":"0.0"},"275":{"cpu":"0.3","mem":"0.1"},"276":{"cpu":"57.0","mem":"2.0"},"278":{"cpu":"1.0","mem":"2.0"},"28":{"cpu":"0.0","mem":"0.0"},"280":{"cpu":"1.3","mem":"2.1"},"283":{"cpu":"0.7","mem":"2.1"},"284":{"cpu":"1.7","mem":"1.9"},"286":{"cpu":"0.0","mem":"0.4"},"288":{"cpu":"1.7","mem":"1.9"},"29":{"cpu":"0.0","mem":"0.0"},"290":{"cpu":"7.3","mem":"2.1"},"293":{"cpu":"0.0","mem":"0.4"},"294":{"cpu":"4.0","mem":"2.1"},"297":{"cpu":"0.0","mem":"0.7"},"30":{"cpu":"0.0","mem":"0.0"},"31":{"cpu":"0.0","mem":"0.0"},"40":{"cpu":"0.0","mem":"0.0"},"41":{"cpu":"0.0","mem":"0.0"},"77":{"cpu":"0.0","mem":"0.0"},"78":{"cpu":"0.0","mem":"0.0"},"79":{"cpu":"0.0","mem":"0.0"},"86":{"cpu":"0.0","mem":"0.0"},"90669":{"cpu":"0.0","mem":"0.0"},"90674":{"cpu":"0.0","mem":"0.0"},"96":{"cpu":"4.0","mem":"4.4"},"MiB":{"cpu":"5870.2","mem":"avail"},"PID":{"cpu":"%CPU","mem":"%MEM"},"Tasks:":{"cpu":"stopped,","mem":"0"},"top":{"cpu":"0","mem":"users,"}},"detection_fps":0.1,"detectors":{"cpu":{"detection_start":0.0,"inference_speed":92.61,"pid":276}},"gpu_usages":{"NVIDIA GeForce GTX 1080":{"gpu":"1 %","mem":"5.7 %"}},"service":{"latest_version":"0.11.1","storage":{"/dev/shm":{"free":255.4,"mount_type":"tmpfs","total":268.4,"used":13.1},"/media/frigate/clips":{"free":12461431.1,"mount_type":"nfs","total":15343341.3,"used":2881910.2},"/media/frigate/recordings":{"free":12461431.1,"mount_type":"nfs","total":15343341.3,"used":2881910.2},"/tmp/cache":{"free":993.4,"mount_type":"tmpfs","total":1000.0,"used":6.6}},"temperatures":{},"uptime":63828,"version":"0.12.0-edbdbb7"}}
Operating system
Other Linux
Install method
Docker CLI
Coral version
Other
Network connection
Wired
Camera make and model
UVC G3 Flex
Any other information that may be helpful
No response
docker run
docker run -d --gpus "device=1" --name frigate_tensor --restart=unless-stopped --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 --device /dev/bus/usb:/dev/bus/usb --shm-size=256m -v /volume2/frigate/_data:/media/frigate -v /volume2/trtmodels:/trt-models -v /var/lib/docker/volumes/frigate_config/_data/config.yml:/config/config.yml:ro -v /etc/localtime:/etc/localtime:ro -e YOLO_MODELS=yolov7-tiny-416 -e FRIGATE_RTSP_PASSWORD='secret' -p 5000:5000 -p 1935:1935 ghcr.io/blakeblackshear/frigate:dev-edbdbb7-tensorrt
Config
mqtt:
host: host
port: 1883
topic_prefix: frigate
client_id: frigate
user: liquidmosq
password: secure
birdseye:
enabled: True
width: 1920
height: 1080
quality: 8
mode: objects
detectors:
tensorrt:
type: tensorrt
model:
path: /trt-models/yolov7-tiny-416.trt
input_tensor: nchw
input_pixel_format: rgb
width: 416
height: 416
cameras:
Front_Garage:
ffmpeg:
output_args:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -ar 44100 -c:a aac
rtmp: -c:v copy -f flv -ar 44100 -c:a aac
inputs:
- path: camera
roles:
- detect
- rtmp
hwaccel_args: preset-nvidia-h264
objects:
track:
- person
- car
- truck
- bicycle
- motorcycle
- dog
- cat
snapshots:
enabled: True
record:
enabled: True
retain:
days: 90
mode: motion
events:
retain:
default: 90
mode: active_objects
motion:
mask:
- 0,151,1280,188,1280,0,0,0
- 680,440,997,720,594,720,388,720,388,451
Back_Alley:
ffmpeg:
output_args:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -ar 44100 -c:a aac
rtmp: -c:v copy -f flv -ar 44100 -c:a aac
inputs:
- path: camera
roles:
- detect
- rtmp
hwaccel_args: preset-nvidia-h264
objects:
track:
- person
- car
- truck
- bicycle
- motorcycle
- dog
- cat
snapshots:
enabled: True
record:
enabled: True
retain:
days: 90
mode: motion
events:
retain:
default: 90
mode: active_objects
motion:
mask:
- 804,720,815,518,733,179,480,0,270,0,114,127,0,253,0,720
- 1109,215,948,186,942,275,1045,420,1186,430,1212,284
- 708,168,1093,165,1182,152,1159,112,599,90
Logs
2023-01-27 17:21:09.806959204 [2023-01-27 17:21:09] frigate.app INFO : Starting Frigate (0.12.0-edbdbb7)
2023-01-27 17:21:09.838088050 [2023-01-27 17:21:09] peewee_migrate INFO : Starting migrations
2023-01-27 17:21:09.866437553 [2023-01-27 17:21:09] peewee_migrate INFO : There is nothing to migrate
2023-01-27 17:21:09.873016487 [2023-01-27 17:21:09] ws4py INFO : Using epoll
2023-01-27 17:21:09.898083354 [2023-01-27 17:21:09] frigate.app INFO : Output process started: 282
2023-01-27 17:21:09.910246110 [2023-01-27 17:21:09] frigate.app INFO : Camera processor started for Front_Garage: 286
2023-01-27 17:21:09.922009726 [2023-01-27 17:21:09] frigate.app INFO : Camera processor started for Back_Alley: 288
2023-01-27 17:21:09.922018966 [2023-01-27 17:21:09] ws4py INFO : Using epoll
2023-01-27 17:21:09.932574655 [2023-01-27 17:21:09] frigate.app INFO : Capture process started for Front_Garage: 290
2023-01-27 17:21:09.933833771 [2023-01-27 17:21:09] frigate.app INFO : Capture process started for Back_Alley: 292
2023-01-27 17:21:10.010501654 [2023-01-27 17:21:09] detector.tensorrt INFO : Starting detection process: 280
2023-01-27 17:21:10.013048388 Process detector:tensorrt:
2023-01-27 17:21:10.013056291 [2023-01-27 17:21:10] frigate.detectors.plugins.tensorrt ERROR : ERROR: failed to load libraries. /trt-models/libyolo_layer.so: cannot open shared object file: No such file or directory
2023-01-27 17:21:10.014000282 Traceback (most recent call last):
2023-01-27 17:21:10.014024129 File "/usr/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
2023-01-27 17:21:10.014026397 self.run()
2023-01-27 17:21:10.014028154 File "/usr/lib/python3.9/multiprocessing/process.py", line 108, in run
2023-01-27 17:21:10.014029822 self._target(*self._args, **self._kwargs)
2023-01-27 17:21:10.014033771 File "/opt/frigate/frigate/object_detection.py", line 97, in run_detector
2023-01-27 17:21:10.014054302 object_detector = LocalObjectDetector(detector_config=detector_config)
2023-01-27 17:21:10.014056692 File "/opt/frigate/frigate/object_detection.py", line 52, in __init__
2023-01-27 17:21:10.014058938 self.detect_api = create_detector(detector_config)
2023-01-27 17:21:10.014060639 File "/opt/frigate/frigate/detectors/__init__.py", line 24, in create_detector
2023-01-27 17:21:10.014062253 return api(detector_config)
2023-01-27 17:21:10.014063990 File "/opt/frigate/frigate/detectors/plugins/tensorrt.py", line 219, in __init__
2023-01-27 17:21:10.014084254 self.engine = self._load_engine(detector_config.model.path)
2023-01-27 17:21:10.014086281 File "/opt/frigate/frigate/detectors/plugins/tensorrt.py", line 87, in _load_engine
2023-01-27 17:21:10.014098672 with open(model_path, "rb") as f, trt.Runtime(self.trt_logger) as runtime:
2023-01-27 17:21:10.014112978 FileNotFoundError: [Errno 2] No such file or directory: '/trt-models/yolov7-tiny-416.trt'
2023-01-27 17:21:10.014114932 Exception ignored in: <function TensorRtDetector.__del__ at 0x7fa852bf3c10>
2023-01-27 17:21:10.014116353 Traceback (most recent call last):
2023-01-27 17:21:10.014118114 File "/opt/frigate/frigate/detectors/plugins/tensorrt.py", line 238, in __del__
2023-01-27 17:21:10.014264893 if self.outputs is not None:
2023-01-27 17:21:10.014268572 AttributeError: 'TensorRtDetector' object has no attribute 'outputs'
Output from this command
docker run --gpus=all --rm -it -v `pwd`/volume2//trtmodels:/tensorrt_models -v `pwd`/tensorrt_models.sh:/tensorrt_models.sh nvcr.io/nvidia/tensorrt:22.07-py3 /tensorrt_models.sh
Im not seeing any data in my trt-models folder
=====================
== NVIDIA TensorRT ==
=====================
NVIDIA Release 22.07 (build 40077977)
NVIDIA TensorRT Version 8.4.1
Copyright (c) 2016-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Container image Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
https://developer.nvidia.com/tensorrt
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
To install Python sample dependencies, run /opt/tensorrt/python/python_setup.sh
To install the open-source samples corresponding to this TensorRT release version
run /opt/tensorrt/install_opensource.sh. To build the open source parsers,
plugins, and samples for current top-of-tree on master or a different branch,
run /opt/tensorrt/install_opensource.sh -b <branch>
See https://github.com/NVIDIA/TensorRT for more information.
+ CUDA_HOME=/usr/local/cuda
+ LD_LIBRARY_PATH=/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
+ OUTPUT_FOLDER=/tensorrt_models
+ echo 'Generating the following TRT Models: yolov4-tiny-288,yolov4-tiny-416,yolov7-tiny-416'
Generating the following TRT Models: yolov4-tiny-288,yolov4-tiny-416,yolov7-tiny-416
+ mkdir -p /tensorrt_models
+ pip install --upgrade pip
Requirement already satisfied: pip in /usr/local/lib/python3.8/dist-packages (22.1.2)
Collecting pip
Downloading pip-22.3.1-py3-none-any.whl (2.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 10.1 MB/s eta 0:00:00
Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 22.1.2
Uninstalling pip-22.1.2:
Successfully uninstalled pip-22.1.2
Successfully installed pip-22.3.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
+ pip install onnx==1.9.0 protobuf==3.20.3
Collecting onnx==1.9.0
Downloading onnx-1.9.0-cp38-cp38-manylinux2010_x86_64.whl (12.2 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.2/12.2 MB 34.6 MB/s eta 0:00:00
Collecting protobuf==3.20.3
Downloading protobuf-3.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)
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Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.8/dist-packages (from onnx==1.9.0) (4.2.0)
Collecting six
Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)
Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.8/dist-packages (from onnx==1.9.0) (1.23.0)
Installing collected packages: six, protobuf, onnx
Attempting uninstall: protobuf
Found existing installation: protobuf 4.21.2
Uninstalling protobuf-4.21.2:
Successfully uninstalled protobuf-4.21.2
Successfully installed onnx-1.9.0 protobuf-3.20.3 six-1.16.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
+ git clone --depth 1 https://github.com/yeahme49/tensorrt_demos.git /tensorrt_demos
Cloning into '/tensorrt_demos'...
remote: Enumerating objects: 118, done.
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remote: Total 118 (delta 12), reused 66 (delta 4), pack-reused 0
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+ cd /tensorrt_demos/plugins
+ make all
computes: 61
NVCCFLAGS: -gencode arch=compute_61,code=[sm_61,compute_61]
nvcc -ccbin g++ -I"/usr/local/cuda/include" -I"/usr/local/TensorRT-7.1.3.4/include" -I"/usr/local/include" -I"plugin" -gencode arch=compute_61,code=[sm_61,compute_61] -Xcompiler -fPIC -c -o yolo_layer.o yolo_layer.cu
yolo_layer.h(89): warning #997-D: function "nvinfer1::IPluginV2Ext::configurePlugin(const nvinfer1::Dims *, int32_t, const nvinfer1::Dims *, int32_t, const nvinfer1::DataType *, const nvinfer1::DataType *, const __nv_bool *, const __nv_bool *, nvinfer1::PluginFormat, int32_t)" is hidden by "nvinfer1::YoloLayerPlugin::configurePlugin" -- virtual function override intended?
yolo_layer.h(89): warning #997-D: function "nvinfer1::IPluginV2Ext::configurePlugin(const nvinfer1::Dims *, int32_t, const nvinfer1::Dims *, int32_t, const nvinfer1::DataType *, const nvinfer1::DataType *, const bool *, const bool *, nvinfer1::PluginFormat, int32_t)" is hidden by "nvinfer1::YoloLayerPlugin::configurePlugin" -- virtual function override intended?
g++ -shared -o libyolo_layer.so yolo_layer.o -L"/usr/local/cuda/lib64" -L"/usr/local/TensorRT-7.1.3.4/lib" -L"/usr/local/lib" -Wl,--start-group -lnvinfer -lnvparsers -lnvinfer_plugin -lcudnn -lcublas -lnvToolsExt -lcudart -lrt -ldl -lpthread -Wl,--end-group
+ cp libyolo_layer.so /tensorrt_models/libyolo_layer.so
+ cd /tensorrt_demos/yolo
+ ./download_yolo.sh
yolov3-tiny.cfg 100%[============================================================================================================>] 1.87K --.-KB/s in 0s
yolov3-tiny.weights 100%[============================================================================================================>] 33.79M 49.0MB/s in 0.7s
yolov3.cfg 100%[============================================================================================================>] 8.15K --.-KB/s in 0s
yolov3.weights 100%[============================================================================================================>] 236.52M 20.1MB/s in 10s
yolov3-spp.cfg 100%[============================================================================================================>] 8.40K --.-KB/s in 0s
yolov3-spp.weights 100%[============================================================================================================>] 240.53M 67.2MB/s in 4.0s
yolov4-tiny.cfg 100%[============================================================================================================>] 3.16K --.-KB/s in 0s
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yolov4.cfg 100%[============================================================================================================>] 11.94K --.-KB/s in 0.001s
yolov4.weights 100%[============================================================================================================>] 245.78M 26.7MB/s in 9.7s
yolov4-csp.cfg 100%[============================================================================================================>] 13.16K --.-KB/s in 0.001s
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yolov4x-mish.weights 100%[============================================================================================================>] 380.89M 4.35MB/s in 50s
yolov4-p5.cfg 100%[============================================================================================================>] 18.94K --.-KB/s in 0.003s
yolov4-p5.weights 100%[============================================================================================================>] 270.53M 12.8MB/s in 16s
yolov7-tiny.cfg 100%[============================================================================================================>] 7.38K --.-KB/s in 0s
yolov7-tiny.weights 100%[============================================================================================================>] 23.81M 5.59MB/s in 4.5s
Creating yolov3-tiny-288.cfg and yolov3-tiny-288.weights
Creating yolov3-tiny-416.cfg and yolov3-tiny-416.weights
Creating yolov3-288.cfg and yolov3-288.weights
Creating yolov3-416.cfg and yolov3-416.weights
Creating yolov3-608.cfg and yolov3-608.weights
Creating yolov3-spp-288.cfg and yolov3-spp-288.weights
Creating yolov3-spp-416.cfg and yolov3-spp-416.weights
Creating yolov3-spp-608.cfg and yolov3-spp-608.weights
Creating yolov4-tiny-288.cfg and yolov4-tiny-288.weights
Creating yolov4-tiny-416.cfg and yolov4-tiny-416.weights
Creating yolov4-288.cfg and yolov4-288.weights
Creating yolov4-416.cfg and yolov4-416.weights
Creating yolov4-608.cfg and yolov4-608.weights
Creating yolov4-csp-256.cfg and yolov4-csp-256.weights
Creating yolov4-csp-512.cfg and yolov4x-csp-512.weights
Creating yolov4x-mish-320.cfg and yolov4x-mish-320.weights
Creating yolov4x-mish-640.cfg and yolov4x-mish-640.weights
Creating yolov4-p5-448.cfg and yolov4-p5-448.weights
Creating yolov4-p5-896.cfg and yolov4-p5-896.weights
Creating yolov7-tiny-288.cfg and yolov7-tiny-288.weights
Creating yolov7-tiny-416.cfg and yolov7-tiny-416.weights
Done.
+ cd /tensorrt_demos/yolo
+ for model in ${YOLO_MODELS//,/ }
+ python3 yolo_to_onnx.py -m yolov4-tiny-288
Parsing DarkNet cfg file...
Building ONNX graph...
graph yolov4-tiny-288 (
%000_net[FLOAT, 1x3x288x288]
) optional inputs with matching initializers (
%001_convolutional_bn_scale[FLOAT, 32]
%001_convolutional_bn_bias[FLOAT, 32]
%001_convolutional_bn_mean[FLOAT, 32]
%001_convolutional_bn_var[FLOAT, 32]
%001_convolutional_conv_weights[FLOAT, 32x3x3x3]
%002_convolutional_bn_scale[FLOAT, 64]
%002_convolutional_bn_bias[FLOAT, 64]
%002_convolutional_bn_mean[FLOAT, 64]
%002_convolutional_bn_var[FLOAT, 64]
%002_convolutional_conv_weights[FLOAT, 64x32x3x3]
%003_convolutional_bn_scale[FLOAT, 64]
%003_convolutional_bn_bias[FLOAT, 64]
%003_convolutional_bn_mean[FLOAT, 64]
%003_convolutional_bn_var[FLOAT, 64]
%003_convolutional_conv_weights[FLOAT, 64x64x3x3]
%005_convolutional_bn_scale[FLOAT, 32]
%005_convolutional_bn_bias[FLOAT, 32]
%005_convolutional_bn_mean[FLOAT, 32]
%005_convolutional_bn_var[FLOAT, 32]
%005_convolutional_conv_weights[FLOAT, 32x32x3x3]
%006_convolutional_bn_scale[FLOAT, 32]
%006_convolutional_bn_bias[FLOAT, 32]
%006_convolutional_bn_mean[FLOAT, 32]
%006_convolutional_bn_var[FLOAT, 32]
%006_convolutional_conv_weights[FLOAT, 32x32x3x3]
%008_convolutional_bn_scale[FLOAT, 64]
%008_convolutional_bn_bias[FLOAT, 64]
%008_convolutional_bn_mean[FLOAT, 64]
%008_convolutional_bn_var[FLOAT, 64]
%008_convolutional_conv_weights[FLOAT, 64x64x1x1]
%011_convolutional_bn_scale[FLOAT, 128]
%011_convolutional_bn_bias[FLOAT, 128]
%011_convolutional_bn_mean[FLOAT, 128]
%011_convolutional_bn_var[FLOAT, 128]
%011_convolutional_conv_weights[FLOAT, 128x128x3x3]
%013_convolutional_bn_scale[FLOAT, 64]
%013_convolutional_bn_bias[FLOAT, 64]
%013_convolutional_bn_mean[FLOAT, 64]
%013_convolutional_bn_var[FLOAT, 64]
%013_convolutional_conv_weights[FLOAT, 64x64x3x3]
%014_convolutional_bn_scale[FLOAT, 64]
%014_convolutional_bn_bias[FLOAT, 64]
%014_convolutional_bn_mean[FLOAT, 64]
%014_convolutional_bn_var[FLOAT, 64]
%014_convolutional_conv_weights[FLOAT, 64x64x3x3]
%016_convolutional_bn_scale[FLOAT, 128]
%016_convolutional_bn_bias[FLOAT, 128]
%016_convolutional_bn_mean[FLOAT, 128]
%016_convolutional_bn_var[FLOAT, 128]
%016_convolutional_conv_weights[FLOAT, 128x128x1x1]
%019_convolutional_bn_scale[FLOAT, 256]
%019_convolutional_bn_bias[FLOAT, 256]
%019_convolutional_bn_mean[FLOAT, 256]
%019_convolutional_bn_var[FLOAT, 256]
%019_convolutional_conv_weights[FLOAT, 256x256x3x3]
%021_convolutional_bn_scale[FLOAT, 128]
%021_convolutional_bn_bias[FLOAT, 128]
%021_convolutional_bn_mean[FLOAT, 128]
%021_convolutional_bn_var[FLOAT, 128]
%021_convolutional_conv_weights[FLOAT, 128x128x3x3]
%022_convolutional_bn_scale[FLOAT, 128]
%022_convolutional_bn_bias[FLOAT, 128]
%022_convolutional_bn_mean[FLOAT, 128]
%022_convolutional_bn_var[FLOAT, 128]
%022_convolutional_conv_weights[FLOAT, 128x128x3x3]
%024_convolutional_bn_scale[FLOAT, 256]
%024_convolutional_bn_bias[FLOAT, 256]
%024_convolutional_bn_mean[FLOAT, 256]
%024_convolutional_bn_var[FLOAT, 256]
%024_convolutional_conv_weights[FLOAT, 256x256x1x1]
%027_convolutional_bn_scale[FLOAT, 512]
%027_convolutional_bn_bias[FLOAT, 512]
%027_convolutional_bn_mean[FLOAT, 512]
%027_convolutional_bn_var[FLOAT, 512]
%027_convolutional_conv_weights[FLOAT, 512x512x3x3]
%028_convolutional_bn_scale[FLOAT, 256]
%028_convolutional_bn_bias[FLOAT, 256]
%028_convolutional_bn_mean[FLOAT, 256]
%028_convolutional_bn_var[FLOAT, 256]
%028_convolutional_conv_weights[FLOAT, 256x512x1x1]
%029_convolutional_bn_scale[FLOAT, 512]
%029_convolutional_bn_bias[FLOAT, 512]
%029_convolutional_bn_mean[FLOAT, 512]
%029_convolutional_bn_var[FLOAT, 512]
%029_convolutional_conv_weights[FLOAT, 512x256x3x3]
%030_convolutional_conv_bias[FLOAT, 255]
%030_convolutional_conv_weights[FLOAT, 255x512x1x1]
%033_convolutional_bn_scale[FLOAT, 128]
%033_convolutional_bn_bias[FLOAT, 128]
%033_convolutional_bn_mean[FLOAT, 128]
%033_convolutional_bn_var[FLOAT, 128]
%033_convolutional_conv_weights[FLOAT, 128x256x1x1]
%034_upsample_scale[FLOAT, 4]
%034_upsample_roi[FLOAT, 4]
%036_convolutional_bn_scale[FLOAT, 256]
%036_convolutional_bn_bias[FLOAT, 256]
%036_convolutional_bn_mean[FLOAT, 256]
%036_convolutional_bn_var[FLOAT, 256]
%036_convolutional_conv_weights[FLOAT, 256x384x3x3]
%037_convolutional_conv_bias[FLOAT, 255]
%037_convolutional_conv_weights[FLOAT, 255x256x1x1]
) {
%001_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%000_net, %001_convolutional_conv_weights)
%001_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%001_convolutional, %001_convolutional_bn_scale, %001_convolutional_bn_bias, %001_convolutional_bn_mean, %001_convolutional_bn_var)
%001_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%001_convolutional_bn)
%002_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%001_convolutional_lrelu, %002_convolutional_conv_weights)
%002_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%002_convolutional, %002_convolutional_bn_scale, %002_convolutional_bn_bias, %002_convolutional_bn_mean, %002_convolutional_bn_var)
%002_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%002_convolutional_bn)
%003_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%002_convolutional_lrelu, %003_convolutional_conv_weights)
%003_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%003_convolutional, %003_convolutional_bn_scale, %003_convolutional_bn_bias, %003_convolutional_bn_mean, %003_convolutional_bn_var)
%003_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%003_convolutional_bn)
%004_route_dummy0, %004_route = Split[axis = 1](%003_convolutional_lrelu)
%005_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%004_route, %005_convolutional_conv_weights)
%005_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%005_convolutional, %005_convolutional_bn_scale, %005_convolutional_bn_bias, %005_convolutional_bn_mean, %005_convolutional_bn_var)
%005_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%005_convolutional_bn)
%006_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%005_convolutional_lrelu, %006_convolutional_conv_weights)
%006_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%006_convolutional, %006_convolutional_bn_scale, %006_convolutional_bn_bias, %006_convolutional_bn_mean, %006_convolutional_bn_var)
%006_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%006_convolutional_bn)
%007_route = Concat[axis = 1](%006_convolutional_lrelu, %005_convolutional_lrelu)
%008_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%007_route, %008_convolutional_conv_weights)
%008_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%008_convolutional, %008_convolutional_bn_scale, %008_convolutional_bn_bias, %008_convolutional_bn_mean, %008_convolutional_bn_var)
%008_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%008_convolutional_bn)
%009_route = Concat[axis = 1](%003_convolutional_lrelu, %008_convolutional_lrelu)
%010_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%009_route)
%011_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%010_maxpool, %011_convolutional_conv_weights)
%011_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%011_convolutional, %011_convolutional_bn_scale, %011_convolutional_bn_bias, %011_convolutional_bn_mean, %011_convolutional_bn_var)
%011_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%011_convolutional_bn)
%012_route_dummy0, %012_route = Split[axis = 1](%011_convolutional_lrelu)
%013_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%012_route, %013_convolutional_conv_weights)
%013_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%013_convolutional, %013_convolutional_bn_scale, %013_convolutional_bn_bias, %013_convolutional_bn_mean, %013_convolutional_bn_var)
%013_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%013_convolutional_bn)
%014_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%013_convolutional_lrelu, %014_convolutional_conv_weights)
%014_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%014_convolutional, %014_convolutional_bn_scale, %014_convolutional_bn_bias, %014_convolutional_bn_mean, %014_convolutional_bn_var)
%014_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%014_convolutional_bn)
%015_route = Concat[axis = 1](%014_convolutional_lrelu, %013_convolutional_lrelu)
%016_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%015_route, %016_convolutional_conv_weights)
%016_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%016_convolutional, %016_convolutional_bn_scale, %016_convolutional_bn_bias, %016_convolutional_bn_mean, %016_convolutional_bn_var)
%016_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%016_convolutional_bn)
%017_route = Concat[axis = 1](%011_convolutional_lrelu, %016_convolutional_lrelu)
%018_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%017_route)
%019_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%018_maxpool, %019_convolutional_conv_weights)
%019_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%019_convolutional, %019_convolutional_bn_scale, %019_convolutional_bn_bias, %019_convolutional_bn_mean, %019_convolutional_bn_var)
%019_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%019_convolutional_bn)
%020_route_dummy0, %020_route = Split[axis = 1](%019_convolutional_lrelu)
%021_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%020_route, %021_convolutional_conv_weights)
%021_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%021_convolutional, %021_convolutional_bn_scale, %021_convolutional_bn_bias, %021_convolutional_bn_mean, %021_convolutional_bn_var)
%021_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%021_convolutional_bn)
%022_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%021_convolutional_lrelu, %022_convolutional_conv_weights)
%022_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%022_convolutional, %022_convolutional_bn_scale, %022_convolutional_bn_bias, %022_convolutional_bn_mean, %022_convolutional_bn_var)
%022_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%022_convolutional_bn)
%023_route = Concat[axis = 1](%022_convolutional_lrelu, %021_convolutional_lrelu)
%024_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%023_route, %024_convolutional_conv_weights)
%024_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%024_convolutional, %024_convolutional_bn_scale, %024_convolutional_bn_bias, %024_convolutional_bn_mean, %024_convolutional_bn_var)
%024_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%024_convolutional_bn)
%025_route = Concat[axis = 1](%019_convolutional_lrelu, %024_convolutional_lrelu)
%026_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%025_route)
%027_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%026_maxpool, %027_convolutional_conv_weights)
%027_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%027_convolutional, %027_convolutional_bn_scale, %027_convolutional_bn_bias, %027_convolutional_bn_mean, %027_convolutional_bn_var)
%027_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%027_convolutional_bn)
%028_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%027_convolutional_lrelu, %028_convolutional_conv_weights)
%028_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%028_convolutional, %028_convolutional_bn_scale, %028_convolutional_bn_bias, %028_convolutional_bn_mean, %028_convolutional_bn_var)
%028_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%028_convolutional_bn)
%029_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%028_convolutional_lrelu, %029_convolutional_conv_weights)
%029_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%029_convolutional, %029_convolutional_bn_scale, %029_convolutional_bn_bias, %029_convolutional_bn_mean, %029_convolutional_bn_var)
%029_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%029_convolutional_bn)
%030_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%029_convolutional_lrelu, %030_convolutional_conv_weights, %030_convolutional_conv_bias)
%033_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%028_convolutional_lrelu, %033_convolutional_conv_weights)
%033_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%033_convolutional, %033_convolutional_bn_scale, %033_convolutional_bn_bias, %033_convolutional_bn_mean, %033_convolutional_bn_var)
%033_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%033_convolutional_bn)
%034_upsample = Resize[coordinate_transformation_mode = 'asymmetric', mode = 'nearest', nearest_mode = 'floor'](%033_convolutional_lrelu, %034_upsample_roi, %034_upsample_scale)
%035_route = Concat[axis = 1](%034_upsample, %024_convolutional_lrelu)
%036_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%035_route, %036_convolutional_conv_weights)
%036_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%036_convolutional, %036_convolutional_bn_scale, %036_convolutional_bn_bias, %036_convolutional_bn_mean, %036_convolutional_bn_var)
%036_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%036_convolutional_bn)
%037_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%036_convolutional_lrelu, %037_convolutional_conv_weights, %037_convolutional_conv_bias)
return %030_convolutional, %037_convolutional
}
Checking ONNX model...
Saving ONNX file...
Done.
+ python3 onnx_to_tensorrt.py -m yolov4-tiny-288
Loading the ONNX file...
Adding yolo_layer plugins.
Adding a concatenated output as "detections".
Naming the input tensort as "input".
Building the TensorRT engine. This would take a while...
(Use "--verbose" or "-v" to enable verbose logging.)
onnx_to_tensorrt.py:147: DeprecationWarning: Use network created with NetworkDefinitionCreationFlag::EXPLICIT_BATCH flag instead.
builder.max_batch_size = MAX_BATCH_SIZE
onnx_to_tensorrt.py:149: DeprecationWarning: Use set_memory_pool_limit instead.
config.max_workspace_size = 1 << 30
onnx_to_tensorrt.py:172: DeprecationWarning: Use build_serialized_network instead.
engine = builder.build_engine(network, config)
[01/27/2023-17:29:33] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.
[01/27/2023-17:29:46] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:46] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:46] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:47] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:47] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:47] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:47] [TRT] [W] Weights [name=005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:47] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:47] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:47] [TRT] [W] Weights [name=005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:47] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:47] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:54] [TRT] [W] Weights [name=006_convolutional + 006_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=014_convolutional + 014_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:55] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:56] [TRT] [W] Weights [name=021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:57] [TRT] [W] Weights [name=022_convolutional + 022_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:57] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:57] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:57] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:57] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:58] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:58] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:58] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:58] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:58] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:58] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:58] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:59] [TRT] [W] Weights [name=029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:59] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:59] [TRT] [W] Weights [name=029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:59] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:59] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:59] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:59] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:59] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:29:59] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:29:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:29:59] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:29:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:00] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
Completed creating engine.
[01/27/2023-17:30:00] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[01/27/2023-17:30:00] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
Serialized the TensorRT engine to file: yolov4-tiny-288.trt
+ cp /tensorrt_demos/yolo/yolov4-tiny-288.trt /tensorrt_models/yolov4-tiny-288.trt
+ for model in ${YOLO_MODELS//,/ }
+ python3 yolo_to_onnx.py -m yolov4-tiny-416
Parsing DarkNet cfg file...
Building ONNX graph...
graph yolov4-tiny-416 (
%000_net[FLOAT, 1x3x416x416]
) optional inputs with matching initializers (
%001_convolutional_bn_scale[FLOAT, 32]
%001_convolutional_bn_bias[FLOAT, 32]
%001_convolutional_bn_mean[FLOAT, 32]
%001_convolutional_bn_var[FLOAT, 32]
%001_convolutional_conv_weights[FLOAT, 32x3x3x3]
%002_convolutional_bn_scale[FLOAT, 64]
%002_convolutional_bn_bias[FLOAT, 64]
%002_convolutional_bn_mean[FLOAT, 64]
%002_convolutional_bn_var[FLOAT, 64]
%002_convolutional_conv_weights[FLOAT, 64x32x3x3]
%003_convolutional_bn_scale[FLOAT, 64]
%003_convolutional_bn_bias[FLOAT, 64]
%003_convolutional_bn_mean[FLOAT, 64]
%003_convolutional_bn_var[FLOAT, 64]
%003_convolutional_conv_weights[FLOAT, 64x64x3x3]
%005_convolutional_bn_scale[FLOAT, 32]
%005_convolutional_bn_bias[FLOAT, 32]
%005_convolutional_bn_mean[FLOAT, 32]
%005_convolutional_bn_var[FLOAT, 32]
%005_convolutional_conv_weights[FLOAT, 32x32x3x3]
%006_convolutional_bn_scale[FLOAT, 32]
%006_convolutional_bn_bias[FLOAT, 32]
%006_convolutional_bn_mean[FLOAT, 32]
%006_convolutional_bn_var[FLOAT, 32]
%006_convolutional_conv_weights[FLOAT, 32x32x3x3]
%008_convolutional_bn_scale[FLOAT, 64]
%008_convolutional_bn_bias[FLOAT, 64]
%008_convolutional_bn_mean[FLOAT, 64]
%008_convolutional_bn_var[FLOAT, 64]
%008_convolutional_conv_weights[FLOAT, 64x64x1x1]
%011_convolutional_bn_scale[FLOAT, 128]
%011_convolutional_bn_bias[FLOAT, 128]
%011_convolutional_bn_mean[FLOAT, 128]
%011_convolutional_bn_var[FLOAT, 128]
%011_convolutional_conv_weights[FLOAT, 128x128x3x3]
%013_convolutional_bn_scale[FLOAT, 64]
%013_convolutional_bn_bias[FLOAT, 64]
%013_convolutional_bn_mean[FLOAT, 64]
%013_convolutional_bn_var[FLOAT, 64]
%013_convolutional_conv_weights[FLOAT, 64x64x3x3]
%014_convolutional_bn_scale[FLOAT, 64]
%014_convolutional_bn_bias[FLOAT, 64]
%014_convolutional_bn_mean[FLOAT, 64]
%014_convolutional_bn_var[FLOAT, 64]
%014_convolutional_conv_weights[FLOAT, 64x64x3x3]
%016_convolutional_bn_scale[FLOAT, 128]
%016_convolutional_bn_bias[FLOAT, 128]
%016_convolutional_bn_mean[FLOAT, 128]
%016_convolutional_bn_var[FLOAT, 128]
%016_convolutional_conv_weights[FLOAT, 128x128x1x1]
%019_convolutional_bn_scale[FLOAT, 256]
%019_convolutional_bn_bias[FLOAT, 256]
%019_convolutional_bn_mean[FLOAT, 256]
%019_convolutional_bn_var[FLOAT, 256]
%019_convolutional_conv_weights[FLOAT, 256x256x3x3]
%021_convolutional_bn_scale[FLOAT, 128]
%021_convolutional_bn_bias[FLOAT, 128]
%021_convolutional_bn_mean[FLOAT, 128]
%021_convolutional_bn_var[FLOAT, 128]
%021_convolutional_conv_weights[FLOAT, 128x128x3x3]
%022_convolutional_bn_scale[FLOAT, 128]
%022_convolutional_bn_bias[FLOAT, 128]
%022_convolutional_bn_mean[FLOAT, 128]
%022_convolutional_bn_var[FLOAT, 128]
%022_convolutional_conv_weights[FLOAT, 128x128x3x3]
%024_convolutional_bn_scale[FLOAT, 256]
%024_convolutional_bn_bias[FLOAT, 256]
%024_convolutional_bn_mean[FLOAT, 256]
%024_convolutional_bn_var[FLOAT, 256]
%024_convolutional_conv_weights[FLOAT, 256x256x1x1]
%027_convolutional_bn_scale[FLOAT, 512]
%027_convolutional_bn_bias[FLOAT, 512]
%027_convolutional_bn_mean[FLOAT, 512]
%027_convolutional_bn_var[FLOAT, 512]
%027_convolutional_conv_weights[FLOAT, 512x512x3x3]
%028_convolutional_bn_scale[FLOAT, 256]
%028_convolutional_bn_bias[FLOAT, 256]
%028_convolutional_bn_mean[FLOAT, 256]
%028_convolutional_bn_var[FLOAT, 256]
%028_convolutional_conv_weights[FLOAT, 256x512x1x1]
%029_convolutional_bn_scale[FLOAT, 512]
%029_convolutional_bn_bias[FLOAT, 512]
%029_convolutional_bn_mean[FLOAT, 512]
%029_convolutional_bn_var[FLOAT, 512]
%029_convolutional_conv_weights[FLOAT, 512x256x3x3]
%030_convolutional_conv_bias[FLOAT, 255]
%030_convolutional_conv_weights[FLOAT, 255x512x1x1]
%033_convolutional_bn_scale[FLOAT, 128]
%033_convolutional_bn_bias[FLOAT, 128]
%033_convolutional_bn_mean[FLOAT, 128]
%033_convolutional_bn_var[FLOAT, 128]
%033_convolutional_conv_weights[FLOAT, 128x256x1x1]
%034_upsample_scale[FLOAT, 4]
%034_upsample_roi[FLOAT, 4]
%036_convolutional_bn_scale[FLOAT, 256]
%036_convolutional_bn_bias[FLOAT, 256]
%036_convolutional_bn_mean[FLOAT, 256]
%036_convolutional_bn_var[FLOAT, 256]
%036_convolutional_conv_weights[FLOAT, 256x384x3x3]
%037_convolutional_conv_bias[FLOAT, 255]
%037_convolutional_conv_weights[FLOAT, 255x256x1x1]
) {
%001_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%000_net, %001_convolutional_conv_weights)
%001_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%001_convolutional, %001_convolutional_bn_scale, %001_convolutional_bn_bias, %001_convolutional_bn_mean, %001_convolutional_bn_var)
%001_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%001_convolutional_bn)
%002_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%001_convolutional_lrelu, %002_convolutional_conv_weights)
%002_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%002_convolutional, %002_convolutional_bn_scale, %002_convolutional_bn_bias, %002_convolutional_bn_mean, %002_convolutional_bn_var)
%002_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%002_convolutional_bn)
%003_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%002_convolutional_lrelu, %003_convolutional_conv_weights)
%003_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%003_convolutional, %003_convolutional_bn_scale, %003_convolutional_bn_bias, %003_convolutional_bn_mean, %003_convolutional_bn_var)
%003_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%003_convolutional_bn)
%004_route_dummy0, %004_route = Split[axis = 1](%003_convolutional_lrelu)
%005_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%004_route, %005_convolutional_conv_weights)
%005_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%005_convolutional, %005_convolutional_bn_scale, %005_convolutional_bn_bias, %005_convolutional_bn_mean, %005_convolutional_bn_var)
%005_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%005_convolutional_bn)
%006_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%005_convolutional_lrelu, %006_convolutional_conv_weights)
%006_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%006_convolutional, %006_convolutional_bn_scale, %006_convolutional_bn_bias, %006_convolutional_bn_mean, %006_convolutional_bn_var)
%006_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%006_convolutional_bn)
%007_route = Concat[axis = 1](%006_convolutional_lrelu, %005_convolutional_lrelu)
%008_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%007_route, %008_convolutional_conv_weights)
%008_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%008_convolutional, %008_convolutional_bn_scale, %008_convolutional_bn_bias, %008_convolutional_bn_mean, %008_convolutional_bn_var)
%008_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%008_convolutional_bn)
%009_route = Concat[axis = 1](%003_convolutional_lrelu, %008_convolutional_lrelu)
%010_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%009_route)
%011_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%010_maxpool, %011_convolutional_conv_weights)
%011_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%011_convolutional, %011_convolutional_bn_scale, %011_convolutional_bn_bias, %011_convolutional_bn_mean, %011_convolutional_bn_var)
%011_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%011_convolutional_bn)
%012_route_dummy0, %012_route = Split[axis = 1](%011_convolutional_lrelu)
%013_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%012_route, %013_convolutional_conv_weights)
%013_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%013_convolutional, %013_convolutional_bn_scale, %013_convolutional_bn_bias, %013_convolutional_bn_mean, %013_convolutional_bn_var)
%013_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%013_convolutional_bn)
%014_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%013_convolutional_lrelu, %014_convolutional_conv_weights)
%014_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%014_convolutional, %014_convolutional_bn_scale, %014_convolutional_bn_bias, %014_convolutional_bn_mean, %014_convolutional_bn_var)
%014_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%014_convolutional_bn)
%015_route = Concat[axis = 1](%014_convolutional_lrelu, %013_convolutional_lrelu)
%016_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%015_route, %016_convolutional_conv_weights)
%016_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%016_convolutional, %016_convolutional_bn_scale, %016_convolutional_bn_bias, %016_convolutional_bn_mean, %016_convolutional_bn_var)
%016_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%016_convolutional_bn)
%017_route = Concat[axis = 1](%011_convolutional_lrelu, %016_convolutional_lrelu)
%018_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%017_route)
%019_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%018_maxpool, %019_convolutional_conv_weights)
%019_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%019_convolutional, %019_convolutional_bn_scale, %019_convolutional_bn_bias, %019_convolutional_bn_mean, %019_convolutional_bn_var)
%019_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%019_convolutional_bn)
%020_route_dummy0, %020_route = Split[axis = 1](%019_convolutional_lrelu)
%021_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%020_route, %021_convolutional_conv_weights)
%021_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%021_convolutional, %021_convolutional_bn_scale, %021_convolutional_bn_bias, %021_convolutional_bn_mean, %021_convolutional_bn_var)
%021_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%021_convolutional_bn)
%022_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%021_convolutional_lrelu, %022_convolutional_conv_weights)
%022_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%022_convolutional, %022_convolutional_bn_scale, %022_convolutional_bn_bias, %022_convolutional_bn_mean, %022_convolutional_bn_var)
%022_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%022_convolutional_bn)
%023_route = Concat[axis = 1](%022_convolutional_lrelu, %021_convolutional_lrelu)
%024_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%023_route, %024_convolutional_conv_weights)
%024_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%024_convolutional, %024_convolutional_bn_scale, %024_convolutional_bn_bias, %024_convolutional_bn_mean, %024_convolutional_bn_var)
%024_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%024_convolutional_bn)
%025_route = Concat[axis = 1](%019_convolutional_lrelu, %024_convolutional_lrelu)
%026_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%025_route)
%027_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%026_maxpool, %027_convolutional_conv_weights)
%027_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%027_convolutional, %027_convolutional_bn_scale, %027_convolutional_bn_bias, %027_convolutional_bn_mean, %027_convolutional_bn_var)
%027_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%027_convolutional_bn)
%028_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%027_convolutional_lrelu, %028_convolutional_conv_weights)
%028_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%028_convolutional, %028_convolutional_bn_scale, %028_convolutional_bn_bias, %028_convolutional_bn_mean, %028_convolutional_bn_var)
%028_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%028_convolutional_bn)
%029_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%028_convolutional_lrelu, %029_convolutional_conv_weights)
%029_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%029_convolutional, %029_convolutional_bn_scale, %029_convolutional_bn_bias, %029_convolutional_bn_mean, %029_convolutional_bn_var)
%029_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%029_convolutional_bn)
%030_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%029_convolutional_lrelu, %030_convolutional_conv_weights, %030_convolutional_conv_bias)
%033_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%028_convolutional_lrelu, %033_convolutional_conv_weights)
%033_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%033_convolutional, %033_convolutional_bn_scale, %033_convolutional_bn_bias, %033_convolutional_bn_mean, %033_convolutional_bn_var)
%033_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%033_convolutional_bn)
%034_upsample = Resize[coordinate_transformation_mode = 'asymmetric', mode = 'nearest', nearest_mode = 'floor'](%033_convolutional_lrelu, %034_upsample_roi, %034_upsample_scale)
%035_route = Concat[axis = 1](%034_upsample, %024_convolutional_lrelu)
%036_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%035_route, %036_convolutional_conv_weights)
%036_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%036_convolutional, %036_convolutional_bn_scale, %036_convolutional_bn_bias, %036_convolutional_bn_mean, %036_convolutional_bn_var)
%036_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%036_convolutional_bn)
%037_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%036_convolutional_lrelu, %037_convolutional_conv_weights, %037_convolutional_conv_bias)
return %030_convolutional, %037_convolutional
}
Checking ONNX model...
Saving ONNX file...
Done.
+ python3 onnx_to_tensorrt.py -m yolov4-tiny-416
Loading the ONNX file...
Adding yolo_layer plugins.
Adding a concatenated output as "detections".
Naming the input tensort as "input".
Building the TensorRT engine. This would take a while...
(Use "--verbose" or "-v" to enable verbose logging.)
onnx_to_tensorrt.py:147: DeprecationWarning: Use network created with NetworkDefinitionCreationFlag::EXPLICIT_BATCH flag instead.
builder.max_batch_size = MAX_BATCH_SIZE
onnx_to_tensorrt.py:149: DeprecationWarning: Use set_memory_pool_limit instead.
config.max_workspace_size = 1 << 30
onnx_to_tensorrt.py:172: DeprecationWarning: Use build_serialized_network instead.
engine = builder.build_engine(network, config)
[01/27/2023-17:30:04] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.
[01/27/2023-17:30:15] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:15] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:15] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:15] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:15] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:15] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:15] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:15] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:15] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:16] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:16] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:16] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:16] [TRT] [W] Weights [name=005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:16] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:16] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:16] [TRT] [W] Weights [name=005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:16] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:16] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:23] [TRT] [W] Weights [name=006_convolutional + 006_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:23] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:23] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:24] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:24] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:24] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:24] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:24] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:24] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:24] [TRT] [W] Weights [name=013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:24] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:24] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:24] [TRT] [W] Weights [name=013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:24] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:24] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:24] [TRT] [W] Weights [name=014_convolutional + 014_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:24] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:24] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=016_convolutional + 016_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:25] [TRT] [W] Weights [name=021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:25] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:25] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:26] [TRT] [W] Weights [name=021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:26] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:26] [TRT] [W] Weights [name=022_convolutional + 022_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:26] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:26] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:26] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:26] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:26] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:26] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:26] [TRT] [W] Weights [name=024_convolutional + 024_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:26] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:27] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:27] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:27] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:27] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:27] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:27] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:27] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:27] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:27] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:27] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:27] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:27] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:27] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:27] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:27] [TRT] [W] Weights [name=028_convolutional + 028_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:27] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:27] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=030_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:28] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:28] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:28] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:29] [TRT] [W] Weights [name=036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:29] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:29] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:29] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:29] [TRT] [W] Weights [name=036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:29] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:29] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:30:29] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:29] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:29] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:29] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:29] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:29] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:29] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:29] [TRT] [W] Weights [name=037_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:29] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:29] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
Completed creating engine.
[01/27/2023-17:30:29] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[01/27/2023-17:30:29] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
Serialized the TensorRT engine to file: yolov4-tiny-416.trt
+ cp /tensorrt_demos/yolo/yolov4-tiny-416.trt /tensorrt_models/yolov4-tiny-416.trt
+ for model in ${YOLO_MODELS//,/ }
+ python3 yolo_to_onnx.py -m yolov7-tiny-416
Parsing DarkNet cfg file...
Building ONNX graph...
graph yolov7-tiny-416 (
%000_net[FLOAT, 1x3x416x416]
) optional inputs with matching initializers (
%001_convolutional_bn_scale[FLOAT, 32]
%001_convolutional_bn_bias[FLOAT, 32]
%001_convolutional_bn_mean[FLOAT, 32]
%001_convolutional_bn_var[FLOAT, 32]
%001_convolutional_conv_weights[FLOAT, 32x3x3x3]
%002_convolutional_bn_scale[FLOAT, 64]
%002_convolutional_bn_bias[FLOAT, 64]
%002_convolutional_bn_mean[FLOAT, 64]
%002_convolutional_bn_var[FLOAT, 64]
%002_convolutional_conv_weights[FLOAT, 64x32x3x3]
%003_convolutional_bn_scale[FLOAT, 32]
%003_convolutional_bn_bias[FLOAT, 32]
%003_convolutional_bn_mean[FLOAT, 32]
%003_convolutional_bn_var[FLOAT, 32]
%003_convolutional_conv_weights[FLOAT, 32x64x1x1]
%005_convolutional_bn_scale[FLOAT, 32]
%005_convolutional_bn_bias[FLOAT, 32]
%005_convolutional_bn_mean[FLOAT, 32]
%005_convolutional_bn_var[FLOAT, 32]
%005_convolutional_conv_weights[FLOAT, 32x64x1x1]
%006_convolutional_bn_scale[FLOAT, 32]
%006_convolutional_bn_bias[FLOAT, 32]
%006_convolutional_bn_mean[FLOAT, 32]
%006_convolutional_bn_var[FLOAT, 32]
%006_convolutional_conv_weights[FLOAT, 32x32x3x3]
%007_convolutional_bn_scale[FLOAT, 32]
%007_convolutional_bn_bias[FLOAT, 32]
%007_convolutional_bn_mean[FLOAT, 32]
%007_convolutional_bn_var[FLOAT, 32]
%007_convolutional_conv_weights[FLOAT, 32x32x3x3]
%009_convolutional_bn_scale[FLOAT, 64]
%009_convolutional_bn_bias[FLOAT, 64]
%009_convolutional_bn_mean[FLOAT, 64]
%009_convolutional_bn_var[FLOAT, 64]
%009_convolutional_conv_weights[FLOAT, 64x128x1x1]
%011_convolutional_bn_scale[FLOAT, 64]
%011_convolutional_bn_bias[FLOAT, 64]
%011_convolutional_bn_mean[FLOAT, 64]
%011_convolutional_bn_var[FLOAT, 64]
%011_convolutional_conv_weights[FLOAT, 64x64x1x1]
%013_convolutional_bn_scale[FLOAT, 64]
%013_convolutional_bn_bias[FLOAT, 64]
%013_convolutional_bn_mean[FLOAT, 64]
%013_convolutional_bn_var[FLOAT, 64]
%013_convolutional_conv_weights[FLOAT, 64x64x1x1]
%014_convolutional_bn_scale[FLOAT, 64]
%014_convolutional_bn_bias[FLOAT, 64]
%014_convolutional_bn_mean[FLOAT, 64]
%014_convolutional_bn_var[FLOAT, 64]
%014_convolutional_conv_weights[FLOAT, 64x64x3x3]
%015_convolutional_bn_scale[FLOAT, 64]
%015_convolutional_bn_bias[FLOAT, 64]
%015_convolutional_bn_mean[FLOAT, 64]
%015_convolutional_bn_var[FLOAT, 64]
%015_convolutional_conv_weights[FLOAT, 64x64x3x3]
%017_convolutional_bn_scale[FLOAT, 128]
%017_convolutional_bn_bias[FLOAT, 128]
%017_convolutional_bn_mean[FLOAT, 128]
%017_convolutional_bn_var[FLOAT, 128]
%017_convolutional_conv_weights[FLOAT, 128x256x1x1]
%019_convolutional_bn_scale[FLOAT, 128]
%019_convolutional_bn_bias[FLOAT, 128]
%019_convolutional_bn_mean[FLOAT, 128]
%019_convolutional_bn_var[FLOAT, 128]
%019_convolutional_conv_weights[FLOAT, 128x128x1x1]
%021_convolutional_bn_scale[FLOAT, 128]
%021_convolutional_bn_bias[FLOAT, 128]
%021_convolutional_bn_mean[FLOAT, 128]
%021_convolutional_bn_var[FLOAT, 128]
%021_convolutional_conv_weights[FLOAT, 128x128x1x1]
%022_convolutional_bn_scale[FLOAT, 128]
%022_convolutional_bn_bias[FLOAT, 128]
%022_convolutional_bn_mean[FLOAT, 128]
%022_convolutional_bn_var[FLOAT, 128]
%022_convolutional_conv_weights[FLOAT, 128x128x3x3]
%023_convolutional_bn_scale[FLOAT, 128]
%023_convolutional_bn_bias[FLOAT, 128]
%023_convolutional_bn_mean[FLOAT, 128]
%023_convolutional_bn_var[FLOAT, 128]
%023_convolutional_conv_weights[FLOAT, 128x128x3x3]
%025_convolutional_bn_scale[FLOAT, 256]
%025_convolutional_bn_bias[FLOAT, 256]
%025_convolutional_bn_mean[FLOAT, 256]
%025_convolutional_bn_var[FLOAT, 256]
%025_convolutional_conv_weights[FLOAT, 256x512x1x1]
%027_convolutional_bn_scale[FLOAT, 256]
%027_convolutional_bn_bias[FLOAT, 256]
%027_convolutional_bn_mean[FLOAT, 256]
%027_convolutional_bn_var[FLOAT, 256]
%027_convolutional_conv_weights[FLOAT, 256x256x1x1]
%029_convolutional_bn_scale[FLOAT, 256]
%029_convolutional_bn_bias[FLOAT, 256]
%029_convolutional_bn_mean[FLOAT, 256]
%029_convolutional_bn_var[FLOAT, 256]
%029_convolutional_conv_weights[FLOAT, 256x256x1x1]
%030_convolutional_bn_scale[FLOAT, 256]
%030_convolutional_bn_bias[FLOAT, 256]
%030_convolutional_bn_mean[FLOAT, 256]
%030_convolutional_bn_var[FLOAT, 256]
%030_convolutional_conv_weights[FLOAT, 256x256x3x3]
%031_convolutional_bn_scale[FLOAT, 256]
%031_convolutional_bn_bias[FLOAT, 256]
%031_convolutional_bn_mean[FLOAT, 256]
%031_convolutional_bn_var[FLOAT, 256]
%031_convolutional_conv_weights[FLOAT, 256x256x3x3]
%033_convolutional_bn_scale[FLOAT, 512]
%033_convolutional_bn_bias[FLOAT, 512]
%033_convolutional_bn_mean[FLOAT, 512]
%033_convolutional_bn_var[FLOAT, 512]
%033_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%034_convolutional_bn_scale[FLOAT, 256]
%034_convolutional_bn_bias[FLOAT, 256]
%034_convolutional_bn_mean[FLOAT, 256]
%034_convolutional_bn_var[FLOAT, 256]
%034_convolutional_conv_weights[FLOAT, 256x512x1x1]
%036_convolutional_bn_scale[FLOAT, 256]
%036_convolutional_bn_bias[FLOAT, 256]
%036_convolutional_bn_mean[FLOAT, 256]
%036_convolutional_bn_var[FLOAT, 256]
%036_convolutional_conv_weights[FLOAT, 256x512x1x1]
%043_convolutional_bn_scale[FLOAT, 256]
%043_convolutional_bn_bias[FLOAT, 256]
%043_convolutional_bn_mean[FLOAT, 256]
%043_convolutional_bn_var[FLOAT, 256]
%043_convolutional_conv_weights[FLOAT, 256x1024x1x1]
%045_convolutional_bn_scale[FLOAT, 256]
%045_convolutional_bn_bias[FLOAT, 256]
%045_convolutional_bn_mean[FLOAT, 256]
%045_convolutional_bn_var[FLOAT, 256]
%045_convolutional_conv_weights[FLOAT, 256x512x1x1]
%046_convolutional_bn_scale[FLOAT, 128]
%046_convolutional_bn_bias[FLOAT, 128]
%046_convolutional_bn_mean[FLOAT, 128]
%046_convolutional_bn_var[FLOAT, 128]
%046_convolutional_conv_weights[FLOAT, 128x256x1x1]
%047_upsample_scale[FLOAT, 4]
%047_upsample_roi[FLOAT, 4]
%049_convolutional_bn_scale[FLOAT, 128]
%049_convolutional_bn_bias[FLOAT, 128]
%049_convolutional_bn_mean[FLOAT, 128]
%049_convolutional_bn_var[FLOAT, 128]
%049_convolutional_conv_weights[FLOAT, 128x256x1x1]
%051_convolutional_bn_scale[FLOAT, 64]
%051_convolutional_bn_bias[FLOAT, 64]
%051_convolutional_bn_mean[FLOAT, 64]
%051_convolutional_bn_var[FLOAT, 64]
%051_convolutional_conv_weights[FLOAT, 64x256x1x1]
%053_convolutional_bn_scale[FLOAT, 64]
%053_convolutional_bn_bias[FLOAT, 64]
%053_convolutional_bn_mean[FLOAT, 64]
%053_convolutional_bn_var[FLOAT, 64]
%053_convolutional_conv_weights[FLOAT, 64x256x1x1]
%054_convolutional_bn_scale[FLOAT, 64]
%054_convolutional_bn_bias[FLOAT, 64]
%054_convolutional_bn_mean[FLOAT, 64]
%054_convolutional_bn_var[FLOAT, 64]
%054_convolutional_conv_weights[FLOAT, 64x64x3x3]
%055_convolutional_bn_scale[FLOAT, 64]
%055_convolutional_bn_bias[FLOAT, 64]
%055_convolutional_bn_mean[FLOAT, 64]
%055_convolutional_bn_var[FLOAT, 64]
%055_convolutional_conv_weights[FLOAT, 64x64x3x3]
%057_convolutional_bn_scale[FLOAT, 128]
%057_convolutional_bn_bias[FLOAT, 128]
%057_convolutional_bn_mean[FLOAT, 128]
%057_convolutional_bn_var[FLOAT, 128]
%057_convolutional_conv_weights[FLOAT, 128x256x1x1]
%058_convolutional_bn_scale[FLOAT, 64]
%058_convolutional_bn_bias[FLOAT, 64]
%058_convolutional_bn_mean[FLOAT, 64]
%058_convolutional_bn_var[FLOAT, 64]
%058_convolutional_conv_weights[FLOAT, 64x128x1x1]
%059_upsample_scale[FLOAT, 4]
%059_upsample_roi[FLOAT, 4]
%061_convolutional_bn_scale[FLOAT, 64]
%061_convolutional_bn_bias[FLOAT, 64]
%061_convolutional_bn_mean[FLOAT, 64]
%061_convolutional_bn_var[FLOAT, 64]
%061_convolutional_conv_weights[FLOAT, 64x128x1x1]
%063_convolutional_bn_scale[FLOAT, 32]
%063_convolutional_bn_bias[FLOAT, 32]
%063_convolutional_bn_mean[FLOAT, 32]
%063_convolutional_bn_var[FLOAT, 32]
%063_convolutional_conv_weights[FLOAT, 32x128x1x1]
%065_convolutional_bn_scale[FLOAT, 32]
%065_convolutional_bn_bias[FLOAT, 32]
%065_convolutional_bn_mean[FLOAT, 32]
%065_convolutional_bn_var[FLOAT, 32]
%065_convolutional_conv_weights[FLOAT, 32x128x1x1]
%066_convolutional_bn_scale[FLOAT, 32]
%066_convolutional_bn_bias[FLOAT, 32]
%066_convolutional_bn_mean[FLOAT, 32]
%066_convolutional_bn_var[FLOAT, 32]
%066_convolutional_conv_weights[FLOAT, 32x32x3x3]
%067_convolutional_bn_scale[FLOAT, 32]
%067_convolutional_bn_bias[FLOAT, 32]
%067_convolutional_bn_mean[FLOAT, 32]
%067_convolutional_bn_var[FLOAT, 32]
%067_convolutional_conv_weights[FLOAT, 32x32x3x3]
%069_convolutional_bn_scale[FLOAT, 64]
%069_convolutional_bn_bias[FLOAT, 64]
%069_convolutional_bn_mean[FLOAT, 64]
%069_convolutional_bn_var[FLOAT, 64]
%069_convolutional_conv_weights[FLOAT, 64x128x1x1]
%070_convolutional_bn_scale[FLOAT, 128]
%070_convolutional_bn_bias[FLOAT, 128]
%070_convolutional_bn_mean[FLOAT, 128]
%070_convolutional_bn_var[FLOAT, 128]
%070_convolutional_conv_weights[FLOAT, 128x64x3x3]
%072_convolutional_bn_scale[FLOAT, 64]
%072_convolutional_bn_bias[FLOAT, 64]
%072_convolutional_bn_mean[FLOAT, 64]
%072_convolutional_bn_var[FLOAT, 64]
%072_convolutional_conv_weights[FLOAT, 64x256x1x1]
%074_convolutional_bn_scale[FLOAT, 64]
%074_convolutional_bn_bias[FLOAT, 64]
%074_convolutional_bn_mean[FLOAT, 64]
%074_convolutional_bn_var[FLOAT, 64]
%074_convolutional_conv_weights[FLOAT, 64x256x1x1]
%075_convolutional_bn_scale[FLOAT, 64]
%075_convolutional_bn_bias[FLOAT, 64]
%075_convolutional_bn_mean[FLOAT, 64]
%075_convolutional_bn_var[FLOAT, 64]
%075_convolutional_conv_weights[FLOAT, 64x64x3x3]
%076_convolutional_bn_scale[FLOAT, 64]
%076_convolutional_bn_bias[FLOAT, 64]
%076_convolutional_bn_mean[FLOAT, 64]
%076_convolutional_bn_var[FLOAT, 64]
%076_convolutional_conv_weights[FLOAT, 64x64x3x3]
%078_convolutional_bn_scale[FLOAT, 128]
%078_convolutional_bn_bias[FLOAT, 128]
%078_convolutional_bn_mean[FLOAT, 128]
%078_convolutional_bn_var[FLOAT, 128]
%078_convolutional_conv_weights[FLOAT, 128x256x1x1]
%079_convolutional_bn_scale[FLOAT, 256]
%079_convolutional_bn_bias[FLOAT, 256]
%079_convolutional_bn_mean[FLOAT, 256]
%079_convolutional_bn_var[FLOAT, 256]
%079_convolutional_conv_weights[FLOAT, 256x128x3x3]
%081_convolutional_bn_scale[FLOAT, 128]
%081_convolutional_bn_bias[FLOAT, 128]
%081_convolutional_bn_mean[FLOAT, 128]
%081_convolutional_bn_var[FLOAT, 128]
%081_convolutional_conv_weights[FLOAT, 128x512x1x1]
%083_convolutional_bn_scale[FLOAT, 128]
%083_convolutional_bn_bias[FLOAT, 128]
%083_convolutional_bn_mean[FLOAT, 128]
%083_convolutional_bn_var[FLOAT, 128]
%083_convolutional_conv_weights[FLOAT, 128x512x1x1]
%084_convolutional_bn_scale[FLOAT, 128]
%084_convolutional_bn_bias[FLOAT, 128]
%084_convolutional_bn_mean[FLOAT, 128]
%084_convolutional_bn_var[FLOAT, 128]
%084_convolutional_conv_weights[FLOAT, 128x128x3x3]
%085_convolutional_bn_scale[FLOAT, 128]
%085_convolutional_bn_bias[FLOAT, 128]
%085_convolutional_bn_mean[FLOAT, 128]
%085_convolutional_bn_var[FLOAT, 128]
%085_convolutional_conv_weights[FLOAT, 128x128x3x3]
%087_convolutional_bn_scale[FLOAT, 256]
%087_convolutional_bn_bias[FLOAT, 256]
%087_convolutional_bn_mean[FLOAT, 256]
%087_convolutional_bn_var[FLOAT, 256]
%087_convolutional_conv_weights[FLOAT, 256x512x1x1]
%089_convolutional_bn_scale[FLOAT, 128]
%089_convolutional_bn_bias[FLOAT, 128]
%089_convolutional_bn_mean[FLOAT, 128]
%089_convolutional_bn_var[FLOAT, 128]
%089_convolutional_conv_weights[FLOAT, 128x64x3x3]
%090_convolutional_conv_bias[FLOAT, 255]
%090_convolutional_conv_weights[FLOAT, 255x128x1x1]
%093_convolutional_bn_scale[FLOAT, 256]
%093_convolutional_bn_bias[FLOAT, 256]
%093_convolutional_bn_mean[FLOAT, 256]
%093_convolutional_bn_var[FLOAT, 256]
%093_convolutional_conv_weights[FLOAT, 256x128x3x3]
%094_convolutional_conv_bias[FLOAT, 255]
%094_convolutional_conv_weights[FLOAT, 255x256x1x1]
%097_convolutional_bn_scale[FLOAT, 512]
%097_convolutional_bn_bias[FLOAT, 512]
%097_convolutional_bn_mean[FLOAT, 512]
%097_convolutional_bn_var[FLOAT, 512]
%097_convolutional_conv_weights[FLOAT, 512x256x3x3]
%098_convolutional_conv_bias[FLOAT, 255]
%098_convolutional_conv_weights[FLOAT, 255x512x1x1]
) {
%001_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%000_net, %001_convolutional_conv_weights)
%001_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%001_convolutional, %001_convolutional_bn_scale, %001_convolutional_bn_bias, %001_convolutional_bn_mean, %001_convolutional_bn_var)
%001_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%001_convolutional_bn)
%002_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%001_convolutional_lrelu, %002_convolutional_conv_weights)
%002_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%002_convolutional, %002_convolutional_bn_scale, %002_convolutional_bn_bias, %002_convolutional_bn_mean, %002_convolutional_bn_var)
%002_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%002_convolutional_bn)
%003_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%002_convolutional_lrelu, %003_convolutional_conv_weights)
%003_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%003_convolutional, %003_convolutional_bn_scale, %003_convolutional_bn_bias, %003_convolutional_bn_mean, %003_convolutional_bn_var)
%003_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%003_convolutional_bn)
%005_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%002_convolutional_lrelu, %005_convolutional_conv_weights)
%005_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%005_convolutional, %005_convolutional_bn_scale, %005_convolutional_bn_bias, %005_convolutional_bn_mean, %005_convolutional_bn_var)
%005_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%005_convolutional_bn)
%006_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%005_convolutional_lrelu, %006_convolutional_conv_weights)
%006_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%006_convolutional, %006_convolutional_bn_scale, %006_convolutional_bn_bias, %006_convolutional_bn_mean, %006_convolutional_bn_var)
%006_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%006_convolutional_bn)
%007_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%006_convolutional_lrelu, %007_convolutional_conv_weights)
%007_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%007_convolutional, %007_convolutional_bn_scale, %007_convolutional_bn_bias, %007_convolutional_bn_mean, %007_convolutional_bn_var)
%007_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%007_convolutional_bn)
%008_route = Concat[axis = 1](%003_convolutional_lrelu, %005_convolutional_lrelu, %006_convolutional_lrelu, %007_convolutional_lrelu)
%009_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%008_route, %009_convolutional_conv_weights)
%009_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%009_convolutional, %009_convolutional_bn_scale, %009_convolutional_bn_bias, %009_convolutional_bn_mean, %009_convolutional_bn_var)
%009_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%009_convolutional_bn)
%010_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%009_convolutional_lrelu)
%011_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%010_maxpool, %011_convolutional_conv_weights)
%011_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%011_convolutional, %011_convolutional_bn_scale, %011_convolutional_bn_bias, %011_convolutional_bn_mean, %011_convolutional_bn_var)
%011_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%011_convolutional_bn)
%013_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%010_maxpool, %013_convolutional_conv_weights)
%013_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%013_convolutional, %013_convolutional_bn_scale, %013_convolutional_bn_bias, %013_convolutional_bn_mean, %013_convolutional_bn_var)
%013_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%013_convolutional_bn)
%014_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%013_convolutional_lrelu, %014_convolutional_conv_weights)
%014_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%014_convolutional, %014_convolutional_bn_scale, %014_convolutional_bn_bias, %014_convolutional_bn_mean, %014_convolutional_bn_var)
%014_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%014_convolutional_bn)
%015_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%014_convolutional_lrelu, %015_convolutional_conv_weights)
%015_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%015_convolutional, %015_convolutional_bn_scale, %015_convolutional_bn_bias, %015_convolutional_bn_mean, %015_convolutional_bn_var)
%015_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%015_convolutional_bn)
%016_route = Concat[axis = 1](%011_convolutional_lrelu, %013_convolutional_lrelu, %014_convolutional_lrelu, %015_convolutional_lrelu)
%017_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%016_route, %017_convolutional_conv_weights)
%017_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%017_convolutional, %017_convolutional_bn_scale, %017_convolutional_bn_bias, %017_convolutional_bn_mean, %017_convolutional_bn_var)
%017_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%017_convolutional_bn)
%018_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%017_convolutional_lrelu)
%019_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%018_maxpool, %019_convolutional_conv_weights)
%019_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%019_convolutional, %019_convolutional_bn_scale, %019_convolutional_bn_bias, %019_convolutional_bn_mean, %019_convolutional_bn_var)
%019_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%019_convolutional_bn)
%021_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%018_maxpool, %021_convolutional_conv_weights)
%021_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%021_convolutional, %021_convolutional_bn_scale, %021_convolutional_bn_bias, %021_convolutional_bn_mean, %021_convolutional_bn_var)
%021_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%021_convolutional_bn)
%022_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%021_convolutional_lrelu, %022_convolutional_conv_weights)
%022_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%022_convolutional, %022_convolutional_bn_scale, %022_convolutional_bn_bias, %022_convolutional_bn_mean, %022_convolutional_bn_var)
%022_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%022_convolutional_bn)
%023_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%022_convolutional_lrelu, %023_convolutional_conv_weights)
%023_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%023_convolutional, %023_convolutional_bn_scale, %023_convolutional_bn_bias, %023_convolutional_bn_mean, %023_convolutional_bn_var)
%023_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%023_convolutional_bn)
%024_route = Concat[axis = 1](%019_convolutional_lrelu, %021_convolutional_lrelu, %022_convolutional_lrelu, %023_convolutional_lrelu)
%025_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%024_route, %025_convolutional_conv_weights)
%025_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%025_convolutional, %025_convolutional_bn_scale, %025_convolutional_bn_bias, %025_convolutional_bn_mean, %025_convolutional_bn_var)
%025_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%025_convolutional_bn)
%026_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [2, 2], strides = [2, 2]](%025_convolutional_lrelu)
%027_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%026_maxpool, %027_convolutional_conv_weights)
%027_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%027_convolutional, %027_convolutional_bn_scale, %027_convolutional_bn_bias, %027_convolutional_bn_mean, %027_convolutional_bn_var)
%027_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%027_convolutional_bn)
%029_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%026_maxpool, %029_convolutional_conv_weights)
%029_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%029_convolutional, %029_convolutional_bn_scale, %029_convolutional_bn_bias, %029_convolutional_bn_mean, %029_convolutional_bn_var)
%029_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%029_convolutional_bn)
%030_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%029_convolutional_lrelu, %030_convolutional_conv_weights)
%030_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%030_convolutional, %030_convolutional_bn_scale, %030_convolutional_bn_bias, %030_convolutional_bn_mean, %030_convolutional_bn_var)
%030_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%030_convolutional_bn)
%031_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%030_convolutional_lrelu, %031_convolutional_conv_weights)
%031_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%031_convolutional, %031_convolutional_bn_scale, %031_convolutional_bn_bias, %031_convolutional_bn_mean, %031_convolutional_bn_var)
%031_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%031_convolutional_bn)
%032_route = Concat[axis = 1](%027_convolutional_lrelu, %029_convolutional_lrelu, %030_convolutional_lrelu, %031_convolutional_lrelu)
%033_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%032_route, %033_convolutional_conv_weights)
%033_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%033_convolutional, %033_convolutional_bn_scale, %033_convolutional_bn_bias, %033_convolutional_bn_mean, %033_convolutional_bn_var)
%033_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%033_convolutional_bn)
%034_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%033_convolutional_lrelu, %034_convolutional_conv_weights)
%034_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%034_convolutional, %034_convolutional_bn_scale, %034_convolutional_bn_bias, %034_convolutional_bn_mean, %034_convolutional_bn_var)
%034_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%034_convolutional_bn)
%036_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%033_convolutional_lrelu, %036_convolutional_conv_weights)
%036_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%036_convolutional, %036_convolutional_bn_scale, %036_convolutional_bn_bias, %036_convolutional_bn_mean, %036_convolutional_bn_var)
%036_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%036_convolutional_bn)
%037_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [5, 5], strides = [1, 1]](%036_convolutional_lrelu)
%039_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [9, 9], strides = [1, 1]](%036_convolutional_lrelu)
%041_maxpool = MaxPool[auto_pad = 'SAME_UPPER', kernel_shape = [13, 13], strides = [1, 1]](%036_convolutional_lrelu)
%042_route = Concat[axis = 1](%041_maxpool, %039_maxpool, %037_maxpool, %036_convolutional_lrelu)
%043_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%042_route, %043_convolutional_conv_weights)
%043_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%043_convolutional, %043_convolutional_bn_scale, %043_convolutional_bn_bias, %043_convolutional_bn_mean, %043_convolutional_bn_var)
%043_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%043_convolutional_bn)
%044_route = Concat[axis = 1](%034_convolutional_lrelu, %043_convolutional_lrelu)
%045_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%044_route, %045_convolutional_conv_weights)
%045_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%045_convolutional, %045_convolutional_bn_scale, %045_convolutional_bn_bias, %045_convolutional_bn_mean, %045_convolutional_bn_var)
%045_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%045_convolutional_bn)
%046_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%045_convolutional_lrelu, %046_convolutional_conv_weights)
%046_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%046_convolutional, %046_convolutional_bn_scale, %046_convolutional_bn_bias, %046_convolutional_bn_mean, %046_convolutional_bn_var)
%046_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%046_convolutional_bn)
%047_upsample = Resize[coordinate_transformation_mode = 'asymmetric', mode = 'nearest', nearest_mode = 'floor'](%046_convolutional_lrelu, %047_upsample_roi, %047_upsample_scale)
%049_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%025_convolutional_lrelu, %049_convolutional_conv_weights)
%049_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%049_convolutional, %049_convolutional_bn_scale, %049_convolutional_bn_bias, %049_convolutional_bn_mean, %049_convolutional_bn_var)
%049_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%049_convolutional_bn)
%050_route = Concat[axis = 1](%049_convolutional_lrelu, %047_upsample)
%051_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%050_route, %051_convolutional_conv_weights)
%051_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%051_convolutional, %051_convolutional_bn_scale, %051_convolutional_bn_bias, %051_convolutional_bn_mean, %051_convolutional_bn_var)
%051_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%051_convolutional_bn)
%053_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%050_route, %053_convolutional_conv_weights)
%053_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%053_convolutional, %053_convolutional_bn_scale, %053_convolutional_bn_bias, %053_convolutional_bn_mean, %053_convolutional_bn_var)
%053_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%053_convolutional_bn)
%054_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%053_convolutional_lrelu, %054_convolutional_conv_weights)
%054_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%054_convolutional, %054_convolutional_bn_scale, %054_convolutional_bn_bias, %054_convolutional_bn_mean, %054_convolutional_bn_var)
%054_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%054_convolutional_bn)
%055_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%054_convolutional_lrelu, %055_convolutional_conv_weights)
%055_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%055_convolutional, %055_convolutional_bn_scale, %055_convolutional_bn_bias, %055_convolutional_bn_mean, %055_convolutional_bn_var)
%055_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%055_convolutional_bn)
%056_route = Concat[axis = 1](%051_convolutional_lrelu, %053_convolutional_lrelu, %054_convolutional_lrelu, %055_convolutional_lrelu)
%057_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%056_route, %057_convolutional_conv_weights)
%057_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%057_convolutional, %057_convolutional_bn_scale, %057_convolutional_bn_bias, %057_convolutional_bn_mean, %057_convolutional_bn_var)
%057_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%057_convolutional_bn)
%058_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%057_convolutional_lrelu, %058_convolutional_conv_weights)
%058_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%058_convolutional, %058_convolutional_bn_scale, %058_convolutional_bn_bias, %058_convolutional_bn_mean, %058_convolutional_bn_var)
%058_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%058_convolutional_bn)
%059_upsample = Resize[coordinate_transformation_mode = 'asymmetric', mode = 'nearest', nearest_mode = 'floor'](%058_convolutional_lrelu, %059_upsample_roi, %059_upsample_scale)
%061_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%017_convolutional_lrelu, %061_convolutional_conv_weights)
%061_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%061_convolutional, %061_convolutional_bn_scale, %061_convolutional_bn_bias, %061_convolutional_bn_mean, %061_convolutional_bn_var)
%061_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%061_convolutional_bn)
%062_route = Concat[axis = 1](%061_convolutional_lrelu, %059_upsample)
%063_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%062_route, %063_convolutional_conv_weights)
%063_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%063_convolutional, %063_convolutional_bn_scale, %063_convolutional_bn_bias, %063_convolutional_bn_mean, %063_convolutional_bn_var)
%063_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%063_convolutional_bn)
%065_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%062_route, %065_convolutional_conv_weights)
%065_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%065_convolutional, %065_convolutional_bn_scale, %065_convolutional_bn_bias, %065_convolutional_bn_mean, %065_convolutional_bn_var)
%065_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%065_convolutional_bn)
%066_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%065_convolutional_lrelu, %066_convolutional_conv_weights)
%066_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%066_convolutional, %066_convolutional_bn_scale, %066_convolutional_bn_bias, %066_convolutional_bn_mean, %066_convolutional_bn_var)
%066_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%066_convolutional_bn)
%067_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%066_convolutional_lrelu, %067_convolutional_conv_weights)
%067_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%067_convolutional, %067_convolutional_bn_scale, %067_convolutional_bn_bias, %067_convolutional_bn_mean, %067_convolutional_bn_var)
%067_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%067_convolutional_bn)
%068_route = Concat[axis = 1](%063_convolutional_lrelu, %065_convolutional_lrelu, %066_convolutional_lrelu, %067_convolutional_lrelu)
%069_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%068_route, %069_convolutional_conv_weights)
%069_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%069_convolutional, %069_convolutional_bn_scale, %069_convolutional_bn_bias, %069_convolutional_bn_mean, %069_convolutional_bn_var)
%069_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%069_convolutional_bn)
%070_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%069_convolutional_lrelu, %070_convolutional_conv_weights)
%070_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%070_convolutional, %070_convolutional_bn_scale, %070_convolutional_bn_bias, %070_convolutional_bn_mean, %070_convolutional_bn_var)
%070_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%070_convolutional_bn)
%071_route = Concat[axis = 1](%070_convolutional_lrelu, %057_convolutional_lrelu)
%072_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%071_route, %072_convolutional_conv_weights)
%072_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%072_convolutional, %072_convolutional_bn_scale, %072_convolutional_bn_bias, %072_convolutional_bn_mean, %072_convolutional_bn_var)
%072_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%072_convolutional_bn)
%074_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%071_route, %074_convolutional_conv_weights)
%074_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%074_convolutional, %074_convolutional_bn_scale, %074_convolutional_bn_bias, %074_convolutional_bn_mean, %074_convolutional_bn_var)
%074_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%074_convolutional_bn)
%075_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%074_convolutional_lrelu, %075_convolutional_conv_weights)
%075_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%075_convolutional, %075_convolutional_bn_scale, %075_convolutional_bn_bias, %075_convolutional_bn_mean, %075_convolutional_bn_var)
%075_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%075_convolutional_bn)
%076_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%075_convolutional_lrelu, %076_convolutional_conv_weights)
%076_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%076_convolutional, %076_convolutional_bn_scale, %076_convolutional_bn_bias, %076_convolutional_bn_mean, %076_convolutional_bn_var)
%076_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%076_convolutional_bn)
%077_route = Concat[axis = 1](%072_convolutional_lrelu, %074_convolutional_lrelu, %075_convolutional_lrelu, %076_convolutional_lrelu)
%078_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%077_route, %078_convolutional_conv_weights)
%078_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%078_convolutional, %078_convolutional_bn_scale, %078_convolutional_bn_bias, %078_convolutional_bn_mean, %078_convolutional_bn_var)
%078_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%078_convolutional_bn)
%079_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%078_convolutional_lrelu, %079_convolutional_conv_weights)
%079_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%079_convolutional, %079_convolutional_bn_scale, %079_convolutional_bn_bias, %079_convolutional_bn_mean, %079_convolutional_bn_var)
%079_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%079_convolutional_bn)
%080_route = Concat[axis = 1](%079_convolutional_lrelu, %045_convolutional_lrelu)
%081_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%080_route, %081_convolutional_conv_weights)
%081_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%081_convolutional, %081_convolutional_bn_scale, %081_convolutional_bn_bias, %081_convolutional_bn_mean, %081_convolutional_bn_var)
%081_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%081_convolutional_bn)
%083_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%080_route, %083_convolutional_conv_weights)
%083_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%083_convolutional, %083_convolutional_bn_scale, %083_convolutional_bn_bias, %083_convolutional_bn_mean, %083_convolutional_bn_var)
%083_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%083_convolutional_bn)
%084_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%083_convolutional_lrelu, %084_convolutional_conv_weights)
%084_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%084_convolutional, %084_convolutional_bn_scale, %084_convolutional_bn_bias, %084_convolutional_bn_mean, %084_convolutional_bn_var)
%084_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%084_convolutional_bn)
%085_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%084_convolutional_lrelu, %085_convolutional_conv_weights)
%085_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%085_convolutional, %085_convolutional_bn_scale, %085_convolutional_bn_bias, %085_convolutional_bn_mean, %085_convolutional_bn_var)
%085_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%085_convolutional_bn)
%086_route = Concat[axis = 1](%081_convolutional_lrelu, %083_convolutional_lrelu, %084_convolutional_lrelu, %085_convolutional_lrelu)
%087_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%086_route, %087_convolutional_conv_weights)
%087_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%087_convolutional, %087_convolutional_bn_scale, %087_convolutional_bn_bias, %087_convolutional_bn_mean, %087_convolutional_bn_var)
%087_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%087_convolutional_bn)
%089_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%069_convolutional_lrelu, %089_convolutional_conv_weights)
%089_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%089_convolutional, %089_convolutional_bn_scale, %089_convolutional_bn_bias, %089_convolutional_bn_mean, %089_convolutional_bn_var)
%089_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%089_convolutional_bn)
%090_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%089_convolutional_lrelu, %090_convolutional_conv_weights, %090_convolutional_conv_bias)
%090_convolutional_lgx = Sigmoid(%090_convolutional)
%093_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%078_convolutional_lrelu, %093_convolutional_conv_weights)
%093_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%093_convolutional, %093_convolutional_bn_scale, %093_convolutional_bn_bias, %093_convolutional_bn_mean, %093_convolutional_bn_var)
%093_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%093_convolutional_bn)
%094_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%093_convolutional_lrelu, %094_convolutional_conv_weights, %094_convolutional_conv_bias)
%094_convolutional_lgx = Sigmoid(%094_convolutional)
%097_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%087_convolutional_lrelu, %097_convolutional_conv_weights)
%097_convolutional_bn = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.990000009536743](%097_convolutional, %097_convolutional_bn_scale, %097_convolutional_bn_bias, %097_convolutional_bn_mean, %097_convolutional_bn_var)
%097_convolutional_lrelu = LeakyRelu[alpha = 0.100000001490116](%097_convolutional_bn)
%098_convolutional = Conv[auto_pad = 'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%097_convolutional_lrelu, %098_convolutional_conv_weights, %098_convolutional_conv_bias)
%098_convolutional_lgx = Sigmoid(%098_convolutional)
return %090_convolutional_lgx, %094_convolutional_lgx, %098_convolutional_lgx
}
Checking ONNX model...
Saving ONNX file...
Done.
+ python3 onnx_to_tensorrt.py -m yolov7-tiny-416
Loading the ONNX file...
Adding yolo_layer plugins.
Adding a concatenated output as "detections".
Naming the input tensort as "input".
Building the TensorRT engine. This would take a while...
(Use "--verbose" or "-v" to enable verbose logging.)
onnx_to_tensorrt.py:147: DeprecationWarning: Use network created with NetworkDefinitionCreationFlag::EXPLICIT_BATCH flag instead.
builder.max_batch_size = MAX_BATCH_SIZE
onnx_to_tensorrt.py:149: DeprecationWarning: Use set_memory_pool_limit instead.
config.max_workspace_size = 1 << 30
onnx_to_tensorrt.py:172: DeprecationWarning: Use build_serialized_network instead.
engine = builder.build_engine(network, config)
[01/27/2023-17:30:33] [TRT] [W] FP16 support requested on hardware without native FP16 support, performance will be negatively affected.
[01/27/2023-17:30:46] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:46] [TRT] [W] Weights [name=002_convolutional + 002_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:46] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn || 005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:46] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn || 005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:46] [TRT] [W] Weights [name=003_convolutional + 003_convolutional_bn || 005_convolutional + 005_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:46] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:46] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=006_convolutional + 006_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=006_convolutional + 006_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=007_convolutional + 007_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=009_convolutional + 009_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=009_convolutional + 009_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=009_convolutional + 009_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:54] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn || 013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:54] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:54] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn || 013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=011_convolutional + 011_convolutional_bn || 013_convolutional + 013_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=014_convolutional + 014_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=014_convolutional + 014_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=015_convolutional + 015_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=017_convolutional + 017_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=017_convolutional + 017_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:55] [TRT] [W] Weights [name=017_convolutional + 017_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:55] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:55] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn || 021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn || 021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=019_convolutional + 019_convolutional_bn || 021_convolutional + 021_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=022_convolutional + 022_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=022_convolutional + 022_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=023_convolutional + 023_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=025_convolutional + 025_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=025_convolutional + 025_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:56] [TRT] [W] Weights [name=025_convolutional + 025_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:56] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:56] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn || 029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn || 029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=027_convolutional + 027_convolutional_bn || 029_convolutional + 029_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=030_convolutional + 030_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=030_convolutional + 030_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:57] [TRT] [W] Weights [name=031_convolutional + 031_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:57] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:57] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.bias] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.bias] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=033_convolutional + 033_convolutional_bn.bias] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=034_convolutional + 034_convolutional_bn || 036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=034_convolutional + 034_convolutional_bn || 036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:58] [TRT] [W] Weights [name=034_convolutional + 034_convolutional_bn || 036_convolutional + 036_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:58] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:58] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:59] [TRT] [W] Weights [name=043_convolutional + 043_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:59] [TRT] [W] Weights [name=043_convolutional + 043_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:30:59] [TRT] [W] Weights [name=043_convolutional + 043_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:30:59] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:30:59] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=045_convolutional + 045_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=045_convolutional + 045_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=045_convolutional + 045_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=046_convolutional + 046_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=046_convolutional + 046_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=046_convolutional + 046_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=049_convolutional + 049_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=049_convolutional + 049_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:00] [TRT] [W] Weights [name=049_convolutional + 049_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:00] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:00] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=051_convolutional + 051_convolutional_bn || 053_convolutional + 053_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=054_convolutional + 054_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=054_convolutional + 054_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=055_convolutional + 055_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=057_convolutional + 057_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=058_convolutional + 058_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=058_convolutional + 058_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:01] [TRT] [W] Weights [name=058_convolutional + 058_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:01] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:01] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=061_convolutional + 061_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=061_convolutional + 061_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=061_convolutional + 061_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=063_convolutional + 063_convolutional_bn || 065_convolutional + 065_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=066_convolutional + 066_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=066_convolutional + 066_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=067_convolutional + 067_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=069_convolutional + 069_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=070_convolutional + 070_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:02] [TRT] [W] Weights [name=070_convolutional + 070_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:02] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:02] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=072_convolutional + 072_convolutional_bn || 074_convolutional + 074_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=075_convolutional + 075_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=076_convolutional + 076_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=078_convolutional + 078_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=079_convolutional + 079_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=079_convolutional + 079_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=081_convolutional + 081_convolutional_bn || 083_convolutional + 083_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=084_convolutional + 084_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=084_convolutional + 084_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=085_convolutional + 085_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:03] [TRT] [W] Weights [name=087_convolutional + 087_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:03] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:03] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:04] [TRT] [W] Weights [name=089_convolutional + 089_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:04] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:04] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:04] [TRT] [W] Weights [name=089_convolutional + 089_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:04] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:04] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:04] [TRT] [W] Weights [name=090_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:04] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:04] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:04] [TRT] [W] Weights [name=090_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:04] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:04] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:04] [TRT] [W] Weights [name=090_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:04] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:04] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=093_convolutional + 093_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=093_convolutional + 093_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=094_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=094_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=094_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=097_convolutional + 097_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:12] [TRT] [W] Weights [name=097_convolutional + 097_convolutional_bn.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:12] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:12] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:13] [TRT] [W] Weights [name=098_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:13] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:13] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:31:13] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:13] [TRT] [W] Weights [name=098_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:13] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:13] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:31:13] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
[01/27/2023-17:31:13] [TRT] [W] Weights [name=098_convolutional.weight] had the following issues when converted to FP16:
[01/27/2023-17:31:13] [TRT] [W] - Subnormal FP16 values detected.
[01/27/2023-17:31:13] [TRT] [W] - Values less than smallest positive FP16 Subnormal value detected. Converting to FP16 minimum subnormalized value.
[01/27/2023-17:31:13] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to reduce the magnitude of the weights.
Completed creating engine.
[01/27/2023-17:31:13] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[01/27/2023-17:31:13] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
Serialized the TensorRT engine to file: yolov7-tiny-416.trt
+ cp /tensorrt_demos/yolo/yolov7-tiny-416.trt /tensorrt_models/yolov7-tiny-416.trt
root@liquidgpu:~#
Im not seeing any data in my trt-models folder
Because you didn't enter it correctly
-v `pwd`/volume2/trtmodels so the folder is wrong
so I didnt realize I wasn't allowed to change the folder location
Of course you are allowed to but then you need to make the subsequent change to the other volume map as well.
I just moved the models generated to my folder I created in volume2/trtmodels
2023-01-27 17:43:55.985589455 [2023-01-27 17:43:55] frigate.app INFO : Starting Frigate (0.12.0-edbdbb7)
2023-01-27 17:43:56.016276767 [2023-01-27 17:43:56] peewee_migrate INFO : Starting migrations
2023-01-27 17:43:56.044510321 [2023-01-27 17:43:56] peewee_migrate INFO : There is nothing to migrate
2023-01-27 17:43:56.051767742 [2023-01-27 17:43:56] ws4py INFO : Using epoll
2023-01-27 17:43:56.077632559 [2023-01-27 17:43:56] frigate.app INFO : Output process started: 278
2023-01-27 17:43:56.092120619 [2023-01-27 17:43:56] ws4py INFO : Using epoll
2023-01-27 17:43:56.092662232 [2023-01-27 17:43:56] frigate.app INFO : Camera processor started for Front_Garage: 284
2023-01-27 17:43:56.098792527 [2023-01-27 17:43:56] frigate.app INFO : Camera processor started for Back_Alley: 287
2023-01-27 17:43:56.108183316 [2023-01-27 17:43:56] frigate.app INFO : Capture process started for Front_Garage: 288
2023-01-27 17:43:56.115850246 [2023-01-27 17:43:56] frigate.app INFO : Capture process started for Back_Alley: 290
2023-01-27 17:43:56.202721868 [2023-01-27 17:43:56] detector.tensorrt INFO : Starting detection process: 277
2023-01-27 17:43:56.960616555 [2023-01-27 17:43:56] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] Init CUDA: CPU +189, GPU +0, now: CPU 243, GPU 194 (MiB)
2023-01-27 17:43:56.976570966 [2023-01-27 17:43:56] frigate.detectors.plugins.tensorrt INFO : Loaded engine size: 34 MiB
2023-01-27 17:43:56.978131403 [2023-01-27 17:43:56] frigate.detectors.plugins.tensorrt WARNING : Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
2023-01-27 17:43:58.031384012 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +193, GPU +304, now: CPU 499, GPU 534 (MiB)
2023-01-27 17:43:58.286059517 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] Init cuDNN: CPU +111, GPU +46, now: CPU 610, GPU 580 (MiB)
2023-01-27 17:43:58.296731006 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +34, now: CPU 0, GPU 34 (MiB)
2023-01-27 17:43:58.296874721 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 575, GPU 572 (MiB)
2023-01-27 17:43:58.296938827 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 575, GPU 580 (MiB)
2023-01-27 17:43:58.297043356 [2023-01-27 17:43:58] frigate.detectors.plugins.tensorrt INFO : [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +12, now: CPU 0, GPU 46 (MiB)
2023-01-27 17:44:12.205042559 [2023-01-27 17:44:12] ws4py INFO : Managing websocket [Local => 127.0.0.1:5002 | Remote => 127.0.0.1:48132]
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