Transfer learning is the process of transferring learned features from one application to another. It is a commonly used training technique where you use a model trained on one task and re-train to use it on a different task.
Train Adapt Optimize (TAO) Toolkit is a simple and easy-to-use Python based AI toolkit for taking purpose-built AI models and customizing them with users' own data.
In this notebook, you will learn how to leverage the simplicity and convenience of TAO to:
This notebook shows an example of classifying gestures using GestureNet in the Train Adapt Optimize (TAO) Toolkit.
Download now
from step one directs you to https://developer.nvidia.com/nvidia-tensorrt-download where you have to Login/Join Now for Nvidia Developer Program MembershipMount your Google drive storage to this Colab instance
try:
import google.colab
%env GOOGLE_COLAB=1
from google.colab import drive
drive.mount('/content/drive', force_remount=True)
except:
%env GOOGLE_COLAB=0
print("Warning: Not a Colab Environment")
env: GOOGLE_COLAB=1 Mounted at /content/drive
# Define model_name
# Available models (#FIXME 1):
# 1. PeopleNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet
# 2. PeopleSemSegNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet
# 3. TrafficCamNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet
# 4. DashCamNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet
# 5. FaceDetectIR - https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir
# 6. FaceDetect - https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetect
# 7. VehicleMakeNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet
# 8. VehicleTypeNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet
# 9. LicensePlateDetection - https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet
# 10. LicensePlateRecognition - https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet
ptm_model_name = "PeopleNet" # FIXME1 (Add the model name from the above mentioned list)
trt_tarfile_path
to the path where you uploaded the TensorRT tar filetrt_untar_folder_path
trt_version
# FIXME 2: set this path of the uploaded TensorRT tar.gz file after browser download
trt_tar_path="/content/drive/MyDrive/TensorRT-8.5.3.1.Linux.x86_64-gnu.cuda-11.8.cudnn8.6.tar.gz"
import os
if not os.path.exists(trt_tar_path):
raise Exception("TAR file not found in the provided path")
# FIXME 3: set to path of the folder where the TensoRT tar.gz file has to be untarred into
%env trt_untar_folder_path=/content/drive/MyDrive/trt_untar
# FIXME 4: set this to the version of TRT you have downloaded
%env trt_version=8.5.3.1
!mkdir -p $trt_untar_folder_path
import os
untar = True
for fname in os.listdir(os.environ.get("trt_untar_folder_path", None)):
if fname.startswith("TensorRT-"+os.environ.get("trt_version")) and not fname.endswith(".tar.gz"):
untar = False
if untar:
!tar -xzf $trt_tar_path -C $trt_untar_folder_path
env: trt_untar_folder_path=/content/drive/MyDrive/trt_untar env: trt_version=8.5.3.1
if not os.path.exists(f'{os.environ.get("trt_untar_folder_path")}/TensorRT-{os.environ.get("trt_version")}'):
raise Exception("TensorRT not untarred properly. Please download and untar properly")
#FIXME 5 - COLAB_NOTEBOOKS_PATH: set this to the path where the repo is to be cloned/repo is already downloaded to
%env COLAB_NOTEBOOKS_PATH=/content/drive/MyDrive/nvidia-tao
if os.environ["GOOGLE_COLAB"] == "1":
if not os.path.exists(os.path.join(os.environ["COLAB_NOTEBOOKS_PATH"])):
!git clone https://github.com/NVIDIA-AI-IOT/nvidia-tao.git $COLAB_NOTEBOOKS_PATH
else:
if not os.path.exists(os.environ["COLAB_NOTEBOOKS_PATH"]):
raise Exception("Error, enter the path of the colab notebooks repo correctly")
!sed -i "s|PATH_TO_TRT|$trt_untar_folder_path|g" $COLAB_NOTEBOOKS_PATH/tao_deploy/setup_env_colab.sh
!sed -i "s|TRT_VERSION|$trt_version|g" $COLAB_NOTEBOOKS_PATH/tao_deploy/setup_env_colab.sh
!sh $COLAB_NOTEBOOKS_PATH/tao_deploy/setup_env_colab.sh
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Done python3.8 is already the newest version (3.8.10-0ubuntu1~20.04.7). python3.8 set to manually installed. 0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded. Reading package lists... Done Building dependency tree Reading state information... Done The following additional packages will be installed: python-pip-whl python3-setuptools python3-wheel Suggested packages: python-setuptools-doc The following NEW packages will be installed: python-pip-whl python3-pip python3-setuptools python3-wheel 0 upgraded, 4 newly installed, 0 to remove and 24 not upgraded. Need to get 2,389 kB of archives. After this operation, 4,933 kB of additional disk space will be used. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Collecting nvidia-eff-tao-encryption==0.1.7 Downloading nvidia_eff_tao_encryption-0.1.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 35.7 MB/s eta 0:00:00 Collecting cryptography (from nvidia-eff-tao-encryption==0.1.7) Downloading cryptography-40.0.2-cp36-abi3-manylinux_2_28_x86_64.whl (3.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.7/3.7 MB 63.5 MB/s eta 0:00:00 Collecting pybind11>=2.6 (from nvidia-eff-tao-encryption==0.1.7) Downloading pybind11-2.10.4-py3-none-any.whl (222 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 222.3/222.3 kB 27.6 MB/s eta 0:00:00 Collecting pyarmor (from nvidia-eff-tao-encryption==0.1.7) Downloading pyarmor-8.2.0-py2.py3-none-any.whl (2.3 MB) 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pyinstaller->nvidia-eff-tao-encryption==0.1.7) Downloading pyinstaller_hooks_contrib-2023.3-py2.py3-none-any.whl (263 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 263.6/263.6 kB 32.6 MB/s eta 0:00:00 Collecting pycparser (from cffi>=1.12->cryptography->nvidia-eff-tao-encryption==0.1.7) Downloading pycparser-2.21-py2.py3-none-any.whl (118 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 118.7/118.7 kB 18.6 MB/s eta 0:00:00 Requirement already satisfied: pip in /usr/local/lib/python3.8/dist-packages (from pip-api->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) (23.1.2) Collecting docopt (from pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading docopt-0.6.2.tar.gz (25 kB) Preparing metadata (setup.py) ... done Collecting yarg (from pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading yarg-0.1.9-py2.py3-none-any.whl (19 kB) Collecting requests (from yarg->pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading requests-2.30.0-py3-none-any.whl (62 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.5/62.5 kB 9.5 MB/s eta 0:00:00 Collecting charset-normalizer<4,>=2 (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (195 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 195.9/195.9 kB 20.5 MB/s eta 0:00:00 Collecting idna<4,>=2.5 (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading idna-3.4-py3-none-any.whl (61 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.5/61.5 kB 9.1 MB/s eta 0:00:00 Collecting urllib3<3,>=1.21.1 (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading urllib3-2.0.2-py3-none-any.whl (123 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 123.2/123.2 kB 18.6 MB/s eta 0:00:00 Collecting certifi>=2017.4.17 (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff-tao-encryption==0.1.7) Downloading certifi-2023.5.7-py3-none-any.whl (156 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 157.0/157.0 kB 23.1 MB/s eta 0:00:00 Building wheels for collected packages: docopt Building wheel for docopt (setup.py) ... done Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=dbc40bea630cb74c07076a1327867e7223d9d9ebf354fbec287e6b0fcdc30002 Stored in directory: /root/.cache/pip/wheels/56/ea/58/ead137b087d9e326852a851351d1debf4ada529b6ac0ec4e8c Successfully built docopt Installing collected packages: pyarmor.cli.core, docopt, altgraph, urllib3, tomli, pyinstaller-hooks-contrib, pycparser, pybind11, pyarmor, pip-api, pathspec, packaging, mypy-extensions, isort, idna, click, charset-normalizer, certifi, requests, pyinstaller, cffi, black, yarg, cryptography, pipreqs, nvidia-eff-tao-encryption Successfully installed altgraph-0.17.3 black-23.3.0 certifi-2023.5.7 cffi-1.15.1 charset-normalizer-3.1.0 click-8.1.3 cryptography-40.0.2 docopt-0.6.2 idna-3.4 isort-4.3.21 mypy-extensions-1.0.0 nvidia-eff-tao-encryption-0.1.7 packaging-23.1 pathspec-0.11.1 pip-api-0.0.30 pipreqs-0.4.13 pyarmor-8.2.0 pyarmor.cli.core-3.2.0 pybind11-2.10.4 pycparser-2.21 pyinstaller-5.11.0 pyinstaller-hooks-contrib-2023.3 requests-2.30.0 tomli-2.0.1 urllib3-2.0.2 yarg-0.1.9 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Collecting nvidia-eff==0.6.2 Downloading nvidia_eff-0.6.2-py38-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (673 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 673.7/673.7 kB 22.9 MB/s eta 0:00:00 Collecting ruamel.yaml (from nvidia-eff==0.6.2) Downloading ruamel.yaml-0.17.26-py3-none-any.whl (109 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 109.1/109.1 kB 16.2 MB/s eta 0:00:00 Requirement already satisfied: pyarmor in /usr/local/lib/python3.8/dist-packages (from nvidia-eff==0.6.2) (8.2.0) Requirement already satisfied: pyinstaller in /usr/local/lib/python3.8/dist-packages (from nvidia-eff==0.6.2) (5.11.0) Collecting wrapt (from nvidia-eff==0.6.2) Downloading wrapt-1.15.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (81 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 81.5/81.5 kB 12.2 MB/s eta 0:00:00 Requirement already satisfied: black in /usr/local/lib/python3.8/dist-packages (from nvidia-eff==0.6.2) (23.3.0) Requirement already satisfied: isort[requirements]<5 in /usr/local/lib/python3.8/dist-packages (from nvidia-eff==0.6.2) (4.3.21) Requirement already satisfied: pipreqs in /usr/local/lib/python3.8/dist-packages (from isort[requirements]<5->nvidia-eff==0.6.2) (0.4.13) Requirement already satisfied: pip-api in /usr/local/lib/python3.8/dist-packages (from isort[requirements]<5->nvidia-eff==0.6.2) (0.0.30) Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (8.1.3) Requirement already satisfied: mypy-extensions>=0.4.3 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (1.0.0) Requirement already satisfied: packaging>=22.0 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (23.1) Requirement already satisfied: pathspec>=0.9.0 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (0.11.1) Requirement already satisfied: platformdirs>=2 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (3.5.1) Requirement already satisfied: tomli>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (2.0.1) Requirement already satisfied: typing-extensions>=3.10.0.0 in /usr/local/lib/python3.8/dist-packages (from black->nvidia-eff==0.6.2) (4.5.0) Requirement already satisfied: pyarmor.cli.core~=3.2.0 in /usr/local/lib/python3.8/dist-packages (from pyarmor->nvidia-eff==0.6.2) (3.2.0) Requirement already satisfied: setuptools>=42.0.0 in /usr/local/lib/python3.8/dist-packages (from pyinstaller->nvidia-eff==0.6.2) (67.7.2) Requirement already satisfied: altgraph in /usr/local/lib/python3.8/dist-packages (from pyinstaller->nvidia-eff==0.6.2) (0.17.3) Requirement already satisfied: pyinstaller-hooks-contrib>=2021.4 in /usr/local/lib/python3.8/dist-packages (from pyinstaller->nvidia-eff==0.6.2) (2023.3) Collecting ruamel.yaml.clib>=0.2.7 (from ruamel.yaml->nvidia-eff==0.6.2) Downloading ruamel.yaml.clib-0.2.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (555 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 555.3/555.3 kB 45.1 MB/s eta 0:00:00 Requirement already satisfied: pip in /usr/local/lib/python3.8/dist-packages (from pip-api->isort[requirements]<5->nvidia-eff==0.6.2) (23.1.2) Requirement already satisfied: docopt in /usr/local/lib/python3.8/dist-packages (from pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (0.6.2) Requirement already satisfied: yarg in /usr/local/lib/python3.8/dist-packages (from pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (0.1.9) Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from yarg->pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (2.30.0) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.8/dist-packages (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (3.1.0) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (3.4) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (2.0.2) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->yarg->pipreqs->isort[requirements]<5->nvidia-eff==0.6.2) (2023.5.7) Installing collected packages: wrapt, ruamel.yaml.clib, ruamel.yaml, nvidia-eff Successfully installed nvidia-eff-0.6.2 ruamel.yaml-0.17.26 ruamel.yaml.clib-0.2.7 wrapt-1.15.0 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Requirement already satisfied: cffi in /usr/local/lib/python3.8/dist-packages (1.15.1) Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi) (2.21) 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Processing ./drive/MyDrive/trt_untar/TensorRT-8.5.3.1/python/tensorrt-8.5.3.1-cp38-none-linux_x86_64.whl Installing collected packages: tensorrt Successfully installed tensorrt-8.5.3.1 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Processing ./drive/MyDrive/trt_untar/TensorRT-8.5.3.1/onnx_graphsurgeon/onnx_graphsurgeon-0.3.12-py2.py3-none-any.whl Collecting numpy (from onnx-graphsurgeon==0.3.12) Using cached numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB) Collecting onnx (from onnx-graphsurgeon==0.3.12) Downloading onnx-1.14.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.6/14.6 MB 86.6 MB/s eta 0:00:00 Collecting protobuf>=3.20.2 (from onnx->onnx-graphsurgeon==0.3.12) Downloading protobuf-4.23.0-cp37-abi3-manylinux2014_x86_64.whl (304 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 304.5/304.5 kB 37.4 MB/s eta 0:00:00 Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.8/dist-packages (from onnx->onnx-graphsurgeon==0.3.12) (4.5.0) Installing collected packages: protobuf, numpy, onnx, onnx-graphsurgeon Successfully installed numpy-1.24.3 onnx-1.14.0 onnx-graphsurgeon-0.3.12 protobuf-4.23.0 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Processing ./drive/MyDrive/trt_untar/TensorRT-8.5.3.1/graphsurgeon/graphsurgeon-0.4.6-py2.py3-none-any.whl Installing collected packages: graphsurgeon Successfully installed graphsurgeon-0.4.6 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Processing ./drive/MyDrive/trt_untar/TensorRT-8.5.3.1/uff/uff-0.6.9-py2.py3-none-any.whl Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.8/dist-packages (from uff==0.6.9) (1.24.3) Requirement already satisfied: protobuf>=3.3.0 in /usr/local/lib/python3.8/dist-packages (from uff==0.6.9) (4.23.0) Installing collected packages: uff Successfully installed uff-0.6.9 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 Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://pypi.ngc.nvidia.com Collecting nvidia-tao-deploy==4.0.0.1 Downloading nvidia_tao_deploy-4.0.0.1-py3-none-any.whl (2.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.5/2.5 MB 11.5 MB/s eta 0:00:00 Collecting Pillow<9.0.0,>=8.1.0 (from nvidia-tao-deploy==4.0.0.1) Downloading Pillow-8.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.1/3.1 MB 104.3 MB/s eta 0:00:00 Collecting PyYAML>=5.1 (from nvidia-tao-deploy==4.0.0.1) Downloading PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (701 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 701.2/701.2 kB 63.8 MB/s eta 0:00:00 Collecting absl-py>=0.7.1 (from nvidia-tao-deploy==4.0.0.1) Downloading absl_py-1.4.0-py3-none-any.whl (126 kB) 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(pyproject.toml) ... done Collecting omegaconf==2.2.2 (from nvidia-tao-deploy==4.0.0.1) Downloading omegaconf-2.2.2-py3-none-any.whl (79 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.1/79.1 kB 12.4 MB/s eta 0:00:00 Requirement already satisfied: onnx in /usr/local/lib/python3.8/dist-packages (from nvidia-tao-deploy==4.0.0.1) (1.14.0) Collecting onnxruntime (from nvidia-tao-deploy==4.0.0.1) Downloading onnxruntime-1.14.1-cp38-cp38-manylinux_2_27_x86_64.whl (5.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.0/5.0 MB 100.8 MB/s eta 0:00:00 Collecting opencv-python (from nvidia-tao-deploy==4.0.0.1) Downloading opencv_python-4.7.0.72-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.8/61.8 MB 10.3 MB/s eta 0:00:00 Collecting protobuf==3.20.1 (from nvidia-tao-deploy==4.0.0.1) Downloading protobuf-3.20.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.0/1.0 MB 76.2 MB/s eta 0:00:00 Collecting pycocotools-fix (from nvidia-tao-deploy==4.0.0.1) Downloading pycocotools-fix-2.0.0.9.tar.gz (124 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.0/124.0 kB 16.4 MB/s eta 0:00:00 Preparing metadata (setup.py) ... done Collecting pynvml==11.0.0 (from nvidia-tao-deploy==4.0.0.1) Downloading pynvml-11.0.0-py3-none-any.whl (46 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.1/46.1 kB 6.9 MB/s eta 0:00:00 Collecting scikit-image==0.17.2 (from nvidia-tao-deploy==4.0.0.1) Downloading scikit_image-0.17.2-cp38-cp38-manylinux1_x86_64.whl (12.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.4/12.4 MB 94.8 MB/s eta 0:00:00 Collecting scikit-learn==0.24.2 (from nvidia-tao-deploy==4.0.0.1) Downloading scikit_learn-0.24.2-cp38-cp38-manylinux2010_x86_64.whl (24.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.9/24.9 MB 67.9 MB/s eta 0:00:00 Collecting scipy==1.5.4 (from nvidia-tao-deploy==4.0.0.1) Downloading scipy-1.5.4-cp38-cp38-manylinux1_x86_64.whl (25.8 MB) 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Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from hydra-core==1.2.0->nvidia-tao-deploy==4.0.0.1) (23.1) Collecting importlib-resources (from hydra-core==1.2.0->nvidia-tao-deploy==4.0.0.1) Using cached importlib_resources-5.12.0-py3-none-any.whl (36 kB) Collecting networkx>=2.0 (from scikit-image==0.17.2->nvidia-tao-deploy==4.0.0.1) Downloading networkx-3.1-py3-none-any.whl (2.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 68.7 MB/s eta 0:00:00 Collecting imageio>=2.3.0 (from scikit-image==0.17.2->nvidia-tao-deploy==4.0.0.1) Downloading imageio-2.28.1-py3-none-any.whl (3.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.4/3.4 MB 75.5 MB/s eta 0:00:00 Collecting tifffile>=2019.7.26 (from scikit-image==0.17.2->nvidia-tao-deploy==4.0.0.1) Downloading tifffile-2023.4.12-py3-none-any.whl (219 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 219.4/219.4 kB 25.0 MB/s eta 0:00:00 Collecting PyWavelets>=1.1.1 (from scikit-image==0.17.2->nvidia-tao-deploy==4.0.0.1) Downloading PyWavelets-1.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.9/6.9 MB 65.0 MB/s eta 0:00:00 Collecting joblib>=0.11 (from scikit-learn==0.24.2->nvidia-tao-deploy==4.0.0.1) Downloading joblib-1.2.0-py3-none-any.whl (297 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 298.0/298.0 kB 37.5 MB/s eta 0:00:00 Collecting threadpoolctl>=2.0.0 (from scikit-learn==0.24.2->nvidia-tao-deploy==4.0.0.1) Downloading threadpoolctl-3.1.0-py3-none-any.whl (14 kB) Collecting pandas (from seaborn==0.7.1->nvidia-tao-deploy==4.0.0.1) Downloading pandas-2.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.3/12.3 MB 101.7 MB/s eta 0:00:00 Collecting contourpy>=1.0.1 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached contourpy-1.0.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (300 kB) Collecting cycler>=0.10 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB) Collecting fonttools>=4.22.0 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached fonttools-4.39.4-py3-none-any.whl (1.0 MB) Collecting kiwisolver>=1.0.1 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached kiwisolver-1.4.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.2 MB) Collecting pyparsing>=2.3.1 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB) Collecting python-dateutil>=2.7 (from matplotlib>=3.0.3->nvidia-tao-deploy==4.0.0.1) Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) INFO: pip is looking at multiple versions of onnx to determine which version is compatible with other requirements. 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Collecting onnx (from nvidia-tao-deploy==4.0.0.1) Downloading onnx-1.13.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 80.3 MB/s eta 0:00:00 Downloading onnx-1.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 65.9 MB/s eta 0:00:00 Downloading onnx-1.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.1/13.1 MB 84.8 MB/s eta 0:00:00 Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.8/dist-packages (from onnx->nvidia-tao-deploy==4.0.0.1) (4.5.0) Collecting coloredlogs (from onnxruntime->nvidia-tao-deploy==4.0.0.1) Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.0/46.0 kB 6.5 MB/s eta 0:00:00 Collecting flatbuffers (from onnxruntime->nvidia-tao-deploy==4.0.0.1) Downloading flatbuffers-23.5.9-py2.py3-none-any.whl (26 kB) Collecting sympy (from onnxruntime->nvidia-tao-deploy==4.0.0.1) Downloading sympy-1.12-py3-none-any.whl (5.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.7/5.7 MB 84.6 MB/s eta 0:00:00 Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.8/dist-packages (from pycocotools-fix->nvidia-tao-deploy==4.0.0.1) (67.7.2) Requirement already satisfied: cython>=0.27.3 in /usr/local/lib/python3.8/dist-packages (from pycocotools-fix->nvidia-tao-deploy==4.0.0.1) (0.29.34) Collecting zipp>=3.1.0 (from importlib-resources->hydra-core==1.2.0->nvidia-tao-deploy==4.0.0.1) Using cached zipp-3.15.0-py3-none-any.whl (6.8 kB) Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime->nvidia-tao-deploy==4.0.0.1) Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 86.8/86.8 kB 14.0 MB/s eta 0:00:00 Collecting pytz>=2020.1 (from pandas->seaborn==0.7.1->nvidia-tao-deploy==4.0.0.1) Downloading pytz-2023.3-py2.py3-none-any.whl (502 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 502.3/502.3 kB 54.8 MB/s eta 0:00:00 Collecting tzdata>=2022.1 (from pandas->seaborn==0.7.1->nvidia-tao-deploy==4.0.0.1) Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 341.8/341.8 kB 39.1 MB/s eta 0:00:00 Collecting mpmath>=0.19 (from sympy->onnxruntime->nvidia-tao-deploy==4.0.0.1) Downloading mpmath-1.3.0-py3-none-any.whl (536 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 58.6 MB/s eta 0:00:00 Building wheels for collected packages: seaborn, antlr4-python3-runtime, mpi4py, pycocotools-fix Building wheel for seaborn (setup.py) ... done Created wheel for seaborn: filename=seaborn-0.7.1-py3-none-any.whl size=165931 sha256=d988941a50b36de022e6ed42b90c981bdcc23bdf38530762c41e6dbdae022f55 Stored in directory: /root/.cache/pip/wheels/97/14/28/123fdaafb903da8a74ad19826a2e800903d5ad8c5fd3be68e1 Building wheel for antlr4-python3-runtime (setup.py) ... done Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144553 sha256=36cffb9882ea98115440aed0e7bea3cfca8ec5882dc09782b80607d35a8f50be Stored in directory: /root/.cache/pip/wheels/b1/a3/c2/6df046c09459b73cc9bb6c4401b0be6c47048baf9a1617c485 Building wheel for mpi4py (pyproject.toml) ... done Created wheel for mpi4py: filename=mpi4py-3.1.4-cp38-cp38-linux_x86_64.whl size=5997259 sha256=dc5114c3111c192608dfc372bcae138bf82e82a75070dfbce4f7e1b99447cda4 Stored in directory: /root/.cache/pip/wheels/f3/35/48/0b9a7076995eea5ea64a7e4bc3f0f342f453080795276264e7 Building wheel for pycocotools-fix (setup.py) ... done Created wheel for pycocotools-fix: filename=pycocotools_fix-2.0.0.9-cp38-cp38-linux_x86_64.whl size=418798 sha256=c597ec0538761ada0334f9b62fd01b018b203b8e3cbb92d254beededd7ca9115 Stored in directory: /root/.cache/pip/wheels/11/44/fb/0194f0a86b9f927546ae5e6e4c964cda0e7ec1504f3e21f21e Successfully built seaborn antlr4-python3-runtime mpi4py pycocotools-fix Installing collected packages: pytz, mpmath, flatbuffers, antlr4-python3-runtime, zipp, tzdata, tqdm, tifffile, threadpoolctl, sympy, six, scipy, PyYAML, PyWavelets, pyparsing, pynvml, protobuf, Pillow, opencv-python, networkx, mpi4py, kiwisolver, joblib, humanfriendly, h5py, fonttools, cycler, contourpy, absl-py, scikit-learn, python-dateutil, onnx, omegaconf, importlib-resources, imageio, coloredlogs, pandas, onnxruntime, matplotlib, hydra-core, seaborn, scikit-image, pycocotools-fix, nvidia-tao-deploy Attempting uninstall: protobuf Found existing installation: protobuf 4.23.0 Uninstalling protobuf-4.23.0: Successfully uninstalled protobuf-4.23.0 Attempting uninstall: onnx Found existing installation: onnx 1.14.0 Uninstalling onnx-1.14.0: Successfully uninstalled onnx-1.14.0 Successfully installed Pillow-8.4.0 PyWavelets-1.4.1 PyYAML-6.0 absl-py-1.4.0 antlr4-python3-runtime-4.9.3 coloredlogs-15.0.1 contourpy-1.0.7 cycler-0.11.0 flatbuffers-23.5.9 fonttools-4.39.4 h5py-3.7.0 humanfriendly-10.0 hydra-core-1.2.0 imageio-2.28.1 importlib-resources-5.12.0 joblib-1.2.0 kiwisolver-1.4.4 matplotlib-3.7.1 mpi4py-3.1.4 mpmath-1.3.0 networkx-3.1 nvidia-tao-deploy-4.0.0.1 omegaconf-2.2.2 onnx-1.12.0 onnxruntime-1.14.1 opencv-python-4.7.0.72 pandas-2.0.1 protobuf-3.20.1 pycocotools-fix-2.0.0.9 pynvml-11.0.0 pyparsing-3.0.9 python-dateutil-2.8.2 pytz-2023.3 scikit-image-0.17.2 scikit-learn-0.24.2 scipy-1.5.4 seaborn-0.7.1 six-1.16.0 sympy-1.12 threadpoolctl-3.1.0 tifffile-2023.4.12 tqdm-4.64.0 tzdata-2023.3 zipp-3.15.0 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
import os
if os.environ.get("LD_LIBRARY_PATH","") == "":
os.environ["LD_LIBRARY_PATH"] = ""
trt_lib_path = f':{os.environ.get("trt_untar_folder_path")}/TensorRT-{os.environ.get("trt_version")}/lib'
os.environ["LD_LIBRARY_PATH"]+=trt_lib_path
# Installing NGC CLI on the local machine.
## Download and install
%env LOCAL_PROJECT_DIR=/ngc_content/
%env CLI=ngccli_cat_linux.zip
!sudo mkdir -p $LOCAL_PROJECT_DIR/ngccli && chmod -R 777 $LOCAL_PROJECT_DIR
# Remove any previously existing CLI installations
!sudo rm -rf $LOCAL_PROJECT_DIR/ngccli/*
!wget "https://ngc.nvidia.com/downloads/$CLI" -P $LOCAL_PROJECT_DIR/ngccli
!unzip -u -q "$LOCAL_PROJECT_DIR/ngccli/$CLI" -d $LOCAL_PROJECT_DIR/ngccli/
!rm $LOCAL_PROJECT_DIR/ngccli/*.zip
os.environ["PATH"]="{}/ngccli/ngc-cli:{}".format(os.getenv("LOCAL_PROJECT_DIR", ""), os.getenv("PATH", ""))
!cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 $LOCAL_PROJECT_DIR/ngccli/ngc-cli/libstdc++.so.6
env: LOCAL_PROJECT_DIR=/ngc_content/ env: CLI=ngccli_cat_linux.zip --2023-05-13 18:56:26-- https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip Resolving ngc.nvidia.com (ngc.nvidia.com)... 18.66.112.75, 18.66.112.122, 18.66.112.117, ... Connecting to ngc.nvidia.com (ngc.nvidia.com)|18.66.112.75|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 42746642 (41M) [application/zip] Saving to: ‘/ngc_content//ngccli/ngccli_cat_linux.zip’ ngccli_cat_linux.zi 100%[===================>] 40.77M 218MB/s in 0.2s 2023-05-13 18:56:26 (218 MB/s) - ‘/ngc_content//ngccli/ngccli_cat_linux.zip’ saved [42746642/42746642]
#List the available versions of the PTM model chosen
modelname_to_available_ptm_map = {
"PeopleNet" : "nvidia/tao/peoplenet:pruned_v*",
"PeopleSemSegNet" : "nvidia/tao/peoplesemsegnet:deployable*",
"TrafficCamNet" : "nvidia/tao/trafficcamnet:pruned_v*",
"DashCamNet" : "nvidia/tao/dashcamnet:pruned_v*",
"VehicleMakeNet" : "nvidia/tao/vehiclemakenet:pruned_v*",
"VehicleTypeNet" : "nvidia/tao/vehicletypenet:pruned_v*",
"LicensePlateRecognition" : "nvidia/tao/lprnet:deployable*",
"LicensePlateDetection" : "nvidia/tao/lpdnet:pruned_v*",
"FaceDetect" : "nvidia/tao/facenet:pruned_v*",
"FaceDetectIR" : "nvidia/tao/facedetectir:pruned_v*"
}
model_to_view_regex = modelname_to_available_ptm_map[ptm_model_name]
# You can see different version of this PTM model
!ngc registry model list $model_to_view_regex
+-------------+----------+--------+------------+-----------+------------------+-----------+-----------------+--------------+ | Version | Accuracy | Epochs | Batch Size | GPU Model | Memory Footprint | File Size | Status | Created Date | +-------------+----------+--------+------------+-----------+------------------+-----------+-----------------+--------------+ | pruned_v2.3 | 86.0 | 120 | 1 | V100 | 8.5 | 8.53 MB | UPLOAD_COMPLETE | Nov 23, 2021 | | pruned_v2.1 | 86.0 | 120 | 1 | V100 | 7.8 | 7.82 MB | UPLOAD_COMPLETE | Aug 24, 2021 | | pruned_v2.0 | 84.3 | 120 | 1 | V100 | 20.9 | 20.9 MB | UPLOAD_COMPLETE | Aug 24, 2021 | | pruned_v1.0 | 83.0 | 120 | 1 | V100 | 11.3 | 11.3 MB | UPLOAD_COMPLETE | Aug 24, 2021 | +-------------+----------+--------+------------+-----------+------------------+-----------+-----------------+--------------+
# FIXME 6 - ptm_download_folder: set the folder path where you want to download the file into
%env ptm_download_folder=/content/drive/MyDrive/ptm_models/
!sudo rm -rf $ptm_download_folder
!sudo mkdir -p $ptm_download_folder && chmod -R 777 $ptm_download_folder
# Optional FIXME 7
# model_to_download_map dictionary_value for chosen PTM key - change the version tag of the PTM model if you want a version different from the below defaults
# Use only the versions which has a etlt file:
# Go to the link of the PTM you chose
# Click 'File Browser tab' to fiew the files for each version
# Choose a version you want to override which contains a etlt file
# 1. PeopleNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet
# 2. PeopleSemSegNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet
# 3. TrafficCamNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet
# 4. DashCamNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet
# 5. FaceDetectIR - https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir
# 6. FaceDetect - https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetect
# 7. VehicleMakeNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet
# 8. VehicleTypeNet - https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet
# 9. LicensePlateDetection - https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet
# 10. LicensePlateRecognition - https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet
model_to_download_map = {
"PeopleNet" : "nvidia/tao/peoplenet:pruned_v2.3",
"PeopleSemSegNet" : "nvidia/tao/peoplesemsegnet:deployable_vanilla_unet_v2.0.1",
"TrafficCamNet" : "nvidia/tao/trafficcamnet:pruned_v1.0.2",
"DashCamNet" : "nvidia/tao/dashcamnet:pruned_v1.0.2",
"VehicleMakeNet" : "nvidia/tao/vehiclemakenet:pruned_v1.0.1",
"VehicleTypeNet" : "nvidia/tao/vehicletypenet:pruned_v1.0.1",
"LicensePlateRecognition" : "nvidia/tao/lprnet:deployable_v1.0",
"LicensePlateDetection" : "nvidia/tao/lpdnet:pruned_v1.0",
"FaceDetect" : "nvidia/tao/facenet:pruned_v2.0",
"FaceDetectIR" : "nvidia/tao/facedetectir:pruned_v1.0.1"
}
model_to_download = model_to_download_map[ptm_model_name]
os.environ["model_to_download"] = model_to_download
!ngc registry model download-version $model_to_download --dest $ptm_download_folder
env: ptm_download_folder=/content/drive/MyDrive/ptm_models/ Downloaded 8.54 MB in 4s, Download speed: 2.13 MB/s -------------------------------------------------------------------------------- Transfer id: peoplenet_vpruned_v2.3 Download status: Completed Downloaded local path: /content/drive/MyDrive/ptm_models/peoplenet_vpruned_v2.3 Total files downloaded: 2 Total downloaded size: 8.54 MB Started at: 2023-05-13 18:57:01.680219 Completed at: 2023-05-13 18:57:05.686773 Duration taken: 4s --------------------------------------------------------------------------------
#FIXME 8 - data_type: choose fp32 or fp16
os.environ["data_type"] = "fp32"
#FIXME 9 - trt_out_folder: choose output folder for TensorRT engine file writing
trt_out_folder = "/content/drive/MyDrive/" + ptm_model_name
!mkdir -p $trt_out_folder
import glob
input_etlt_file_list = glob.glob(os.environ.get("ptm_download_folder")+"/**/*.etlt", recursive=True)
if len(input_etlt_file_list) == 0:
raise Exception("ETLT file was not downloaded")
os.environ["input_etlt_file"] = input_etlt_file_list[0]
if ptm_model_name in ("LicensePlateRecognition","LicensePlateDetection"):
# FIXME: country
# us/ccpd for LicensePlateDetection - us for United States, ch for China
# us/ch for LicensePlateRecongition - us for United States, ch for China
country = "us"
for countrywise_ptm in input_etlt_file_list:
fname = countrywise_ptm.split("/")[-1]
if fname.startswith(country):
os.environ["input_etlt_file"] = countrywise_ptm
action = ""
if ptm_model_name in ("PeopleNet","LicensePlateDetection","DashCamNet","TrafficCamNet","FaceDetect","FaceDetectIR"):
action = "_trt"
os.environ["KEY"] = "tlt_encode"
if ptm_model_name in ("LicensePlateRecognition","LicensePlateDetection","FaceDetect"):
os.environ["KEY"] = "nvidia_tlt"
os.environ["trt_experiment_spec"] = f"{os.environ.get('COLAB_NOTEBOOKS_PATH')}/tao_deploy/specs/{ptm_model_name}/{ptm_model_name}{action}.txt"
os.environ["trt_out_file_name"] = f'{trt_out_folder}/{ptm_model_name}.trt.{os.environ["data_type"]}'
if ptm_model_name in ("PeopleNet","LicensePlateDetection","DashCamNet","TrafficCamNet","FaceDetect","FaceDetectIR"):
!detectnet_v2 gen_trt_engine \
-m $input_etlt_file \
-k $KEY \
-e $trt_experiment_spec \
--data_type $data_type \
--batch_size 1 \
--max_batch_size 1 \
--engine_file $trt_out_file_name
elif ptm_model_name in ("VehicleMakeNet","VehicleTypeNet"):
!classification_tf1 gen_trt_engine \
-m $input_etlt_file \
-k $KEY \
-e $trt_experiment_spec \
--data_type $data_type \
--batch_size 1 \
--max_batch_size 1 \
--batches 10 \
--engine_file $trt_out_file_name
elif ptm_model_name == "PeopleSemSegNet":
!unet gen_trt_engine \
-m $input_etlt_file \
-k $KEY \
-e $trt_experiment_spec \
--data_type $data_type \
--batch_size 1 \
--max_batch_size 3 \
--engine_file $trt_out_file_name
elif ptm_model_name == "LicensePlateRecognition":
!lprnet gen_trt_engine \
-m $input_etlt_file \
-k $KEY \
--data_type $data_type \
--min_batch_size 1 \
--opt_batch_size 4 \
--max_batch_size 16 \
--engine_file $trt_out_file_name
/usr/local/lib/python3.8/dist-packages/pycuda/compyte/dtypes.py:120: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar. reg.get_or_register_dtype("bool", np.bool) Traceback (most recent call last): File "/usr/local/bin/detectnet_v2", line 8, in <module> sys.exit(main()) File "<frozen cv.detectnet_v2.entrypoint.detectnet_v2>", line 12, in main File "<frozen cv.common.entrypoint.entrypoint_proto>", line 196, in launch_job File "<frozen cv.common.entrypoint.entrypoint_proto>", line 50, in get_modules File "/usr/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 848, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "</usr/local/lib/python3.8/dist-packages/nvidia_tao_deploy/cv/detectnet_v2/scripts/evaluate.py>", line 3, in <module> File "<frozen cv.detectnet_v2.scripts.evaluate>", line 26, in <module> File "</usr/local/lib/python3.8/dist-packages/nvidia_tao_deploy/cv/detectnet_v2/inferencer.py>", line 1, in <module> File "<frozen cv.detectnet_v2.inferencer>", line 50, in <module> File "</usr/local/lib/python3.8/dist-packages/nvidia_tao_deploy/inferencer/utils.py>", line 1, in <module> File "<frozen inferencer.utils>", line 15, in <module> File "/usr/local/lib/python3.8/dist-packages/pycuda/autoinit.py", line 7, in <module> from pycuda.tools import make_default_context File "/usr/local/lib/python3.8/dist-packages/pycuda/tools.py", line 49, in <module> _fill_dtype_registry(respect_windows=True) File "/usr/local/lib/python3.8/dist-packages/pycuda/compyte/dtypes.py", line 221, in _fill_dtype_registry fill_registry_with_c_types( File "/usr/local/lib/python3.8/dist-packages/pycuda/compyte/dtypes.py", line 120, in fill_registry_with_c_types reg.get_or_register_dtype("bool", np.bool) File "/usr/local/lib/python3.8/dist-packages/numpy/__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'bool'. `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if ptm_model_name in ("PeopleNet","LicensePlateDetection","DashCamNet","TrafficCamNet","FaceDetect","FaceDetectIR"):
action = "_infer"
os.environ["inference_experiment_spec"] = f"{os.environ.get('COLAB_NOTEBOOKS_PATH')}/tao_deploy/specs/{ptm_model_name}/{ptm_model_name}{action}.txt"
# FIXME 10 - inference_out_folder: Folder path to write the inference results to
os.environ["inference_out_folder"] = "/content/drive/MyDrive/tao_ptm_inference/"
!rm -rf $inference_out_folder
# FIXME 11 - inference_input_images_folder: Folder path containing images to run the inference on
os.environ["inference_input_images_folder"] = f"/content/drive/MyDrive/tao_deploy_input_images/{ptm_model_name}"
print(f"Running inference for {ptm_model_name} on {os.environ['trt_out_file_name']}")
if ptm_model_name in ("PeopleNet","LicensePlateDetection","DashCamNet","TrafficCamNet","FaceDetect","FaceDetectIR"):
!detectnet_v2 inference -e $inference_experiment_spec \
-m $trt_out_file_name \
-r $inference_out_folder \
-i $inference_input_images_folder
elif ptm_model_name in ("VehicleMakeNet","VehicleTypeNet"):
os.environ["inference_classmap"] = f"{os.environ.get('COLAB_NOTEBOOKS_PATH')}/tao_deploy/specs/{ptm_model_name}/classmap.json"
!classification_tf1 inference -e $inference_experiment_spec \
-m $trt_out_file_name \
-r $inference_out_folder \
-c $inference_classmap \
-i $inference_input_images_folder
elif ptm_model_name == "PeopleSemSegNet":
# Write path of images to a file - it is required for PeopleSemSegNet which is based on unet
with open("/content/PeopleSemSegNet_inference.txt","w") as file_ptr:
for image_name in os.listdir(os.environ["inference_input_images_folder"]):
if image_name.endswith(".jpg") or image_name.endswith(".png"):
file_ptr.write(os.environ["inference_input_images_folder"]+"/"+image_name+"\n")
file_ptr.flush()
!unet inference -e $inference_experiment_spec \
-m $trt_out_file_name \
-r $inference_out_folder
elif ptm_model_name == "LicensePlateRecognition":
character_file_link = "https://api.ngc.nvidia.com/v2/models/nvidia/tao/lprnet/versions/trainable_v1.0/files/{}_lp_characters.txt".format(country)
!wget -q -O /content/characters.txt $character_file_link
!lprnet inference -i $inference_input_images_folder \
-e $inference_experiment_spec \
-m $trt_out_file_name
if ptm_model_name in ("VehicleMakeNet", "VehicleTypeNet"):
import pandas as pd
dataframe = pd.read_csv(os.environ["inference_out_folder"]+ "/result.csv", header=None)
with pd.option_context('display.max_rows', None, 'display.max_columns', None, 'display.max_colwidth', None):
print(dataframe)
elif ptm_model_name != "LicensePlateRecognition":
from IPython.display import Image, display
import glob
subfolder = ""
if ptm_model_name == "PeopleSemSegNet":
subfolder = "vis_overlay"
inference_out_images_png = glob.glob(f'{os.environ["inference_out_folder"]}/{subfolder}/**/*.png', recursive=True)
inference_out_images_png = glob.glob(f'{os.environ["inference_out_folder"]}/{subfolder}/**/*.jpeg', recursive=True)
inference_out_images_jpg = glob.glob(f'{os.environ["inference_out_folder"]}/{subfolder}/**/*.jpg', recursive=True)
inference_out_images = inference_out_images_png + inference_out_images_jpg
if len(inference_out_images) == 0:
raise Exception("Run Inference before visualization")
display(Image(inference_out_images[0]))