Options: -h, –help Show this help message and exit. -m_encoder M_ENCODER Required. Path to an .xml file with a trained encoder part of the model -m_decoder M_DECODER Required. Path to an .xml file with a trained decoder part of the model -i INPUT, –input INPUT Required. Path to a folder with images or path to an image files -o OUTPUT_FILE, –output_file OUTPUT_FILE Optional. Path to file where to store output. If not mentioned, result will be storedin the console. –vocab_path VOCAB_PATH Required. Path to vocab file to construct meaningful phrase
# This file can be used with the --list option of the model downloader.
forward-tacotron-duration-prediction
forward-tacotron-regression
wavernn-rnn
wavernn-upsampler
ヘルプの確認
% python3 text_to_speech_demo.py -h
usage: text_to_speech_demo.py [-h] -m_duration MODEL_DURATION -m_forward
MODEL_FORWARD -m_upsample MODEL_UPSAMPLE -m_rnn
MODEL_RNN -i INPUT [-o OUT]
[--upsampler_width UPSAMPLER_WIDTH] [-d DEVICE]
Options:
-h, --help Show this help message and exit.
-m_duration MODEL_DURATION, --model_duration MODEL_DURATION
Required. Path to ForwardTacotron`s duration
prediction part (*.xml format).
-m_forward MODEL_FORWARD, --model_forward MODEL_FORWARD
Required. Path to ForwardTacotron`s mel-spectrogram
regression part (*.xml format).
-m_upsample MODEL_UPSAMPLE, --model_upsample MODEL_UPSAMPLE
Required. Path to WaveRNN`s part for mel-spectrogram
upsampling by time axis (*.xml format).
-m_rnn MODEL_RNN, --model_rnn MODEL_RNN
Required. Path to WaveRNN`s part for waveform
autoregression (*.xml format).
-i INPUT, --input INPUT
Text file with text.
-o OUT, --out OUT Required. Path to an output .wav file
--upsampler_width UPSAMPLER_WIDTH
Width for reshaping of the model_upsample. If -1 then
no reshape. Do not use with FP16 model.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL, MYRIAD or HETERO is acceptable. The
sample will look for a suitable plugin for device
specified. Default value is CPU
実行してみます
テキストファイルを読み込ませると、movで出力されます。まずはVincent van Gogh （ゴッホ）さんの名言を喋らせてみましょう。
Your life would be very empty if you had nothing to regret.
Your time is limited, so don't waste it living someone else's life. Don't be trapped by dogma — which is living with the results of other people's thinking. Don't let the noise of others' opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition. They somehow already know what you truly want to become. Everything else is secondary.
# This file can be used with the --list option of the model downloader.
colorization-v2
colorization-siggraph
ヘルプの確認
% python3 colorization_demo.py -h
usage: colorization_demo.py [-h] -m MODEL [-d DEVICE] -i "<path>" [--no_show]
[-v] [-u UTILIZATION_MONITORS]
Options:
-h, --help Help with the script.
-m MODEL, --model MODEL
Required. Path to .xml file with pre-trained model.
-d DEVICE, --device DEVICE
Optional. Specify target device for infer: CPU, GPU,
FPGA, HDDL or MYRIAD. Default: CPU
-i "<path>", --input "<path>"
Required. Input to process.
--no_show Optional. Disable display of results on screen.
-v, --verbose Optional. Enable display of processing logs on screen.
-u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
Optional. List of monitors to show initially.
# This file can be used with the --list option of the model downloader.
fcrn-dp-nyu-depth-v2-tf
midasnet
ヘルプの確認
% python3 monodepth_demo.py -h
usage: monodepth_demo.py [-h] -m MODEL -i INPUT [-l CPU_EXTENSION] [-d DEVICE]
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model
-i INPUT, --input INPUT
Required. Path to a input image file
-l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
Optional. Required for CPU custom layers. Absolute
MKLDNN (CPU)-targeted custom layers. Absolute path to
a shared library with the kernels implementations
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. Sample will
look for a suitable plugin for device specified.
Default value is CPU
実行してみる
入力画像は以下を使いました。Suzuki Bandit 250vです。
モデル：fcrn-dp-nyu-depth-v2-tf で実行
% python3 monodepth_demo.py -m /public/fcrn-dp-nyu-depth-v2-tf/FP16/fcrn-dp-nyu-depth-v2-tf.xml -i IMG_0958.jpg
[ INFO ] creating inference engine
[ INFO ] Loading network
[ INFO ] preparing input blobs
[ INFO ] Image is resized from (768, 1024) to (228, 304)
[ INFO ] loading model to the plugin
[ INFO ] starting inference
[ INFO ] processing output blob
[ INFO ] Disparity map was saved to disp.pfm
[ INFO ] Color-coded disparity image was saved to disp.png
[ INFO ] This demo is an API example, for any performance measurements please use the dedicated benchmark_app tool from the openVINO toolkit
モデル：midasnet で実行
% python3 monodepth_demo.py -i IMG_0958.jpg -m /public/midasnet/FP16/midasnet.xml
[ INFO ] creating inference engine
[ INFO ] Loading network
[ INFO ] preparing input blobs
[ INFO ] Image is resized from (768, 1024) to (384, 384)
[ INFO ] loading model to the plugin
[ INFO ] starting inference
[ INFO ] processing output blob
[ INFO ] Disparity map was saved to disp.pfm
[ INFO ] Color-coded disparity image was saved to disp.png
[ INFO ] This demo is an API example, for any performance measurements please use the dedicated benchmark_app tool from the openVINO toolkit
Last login: Thu Dec 24 18:34:05 on ttys000
[setupvars.sh] WARNING: Can not find OpenVINO Python binaries by path /Users/python
[setupvars.sh] WARNING: OpenVINO Python environment does not set properly
[setupvars.sh] OpenVINO environment initialized
Last login: Fri Dec 25 10:12:12 on ttys000
[setupvars.sh] WARNING: Can not find OpenVINO Python module for python3.9 by path /opt/intel/openvino_2021/python/python3.9
[setupvars.sh] WARNING: OpenVINO Python environment does not set properly
[setupvars.sh] OpenVINO environment initialized
% python3
Python 3.7.7 (default, Nov 12 2020, 17:58:53)
[Clang 12.0.0 (clang-1200.0.32.21)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
>>>
human_pose_estimation.pyの実行
human_pose_estimation.pyを実行してみます。
% python3 human_pose_estimation.py -h
Traceback (most recent call last):
File "human_pose_estimation.py", line 30, in <module>
from human_pose_estimation_demo.model import HPEAssociativeEmbedding, HPEOpenPose
File "/Users//omz_demos_build/python_demos/human_pose_estimation_demo/human_pose_estimation_demo/model.py", line 23, in <module>
import ngraph as ng
File "/opt/intel/openvino_2021/python/python3.7/ngraph/__init__.py", line 26, in <module>
from ngraph.impl import Node
File "/opt/intel/openvino_2021/python/python3.7/ngraph/impl/__init__.py", line 35, in <module>
from _pyngraph import Dimension
ImportError: dlopen(/opt/intel/openvino_2021/python/python3.7/_pyngraph.cpython-37m-darwin.so, 2): Library not loaded: @rpath/libonnx_importer.dylib
Referenced from: /opt/intel/openvino_2021/python/python3.7/_pyngraph.cpython-37m-darwin.so
Reason: image not found
human_pose_estimation_demo % python3 human_pose_estimation.py -h
usage: human_pose_estimation.py [-h] -i INPUT -m MODEL -at {ae,openpose}
[--tsize TSIZE] [-t PROB_THRESHOLD] [-r]
[-d DEVICE] [-nireq NUM_INFER_REQUESTS]
[-nstreams NUM_STREAMS]
[-nthreads NUM_THREADS] [-loop LOOP]
[-no_show] [-u UTILIZATION_MONITORS]
Options:
-h, --help Show this help message and exit.
-i INPUT, --input INPUT
Required. Path to an image, video file or a numeric
camera ID.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-at {ae,openpose}, --architecture_type {ae,openpose}
Required. Type of the network, either "ae" for
Associative Embedding or "openpose" for OpenPose.
--tsize TSIZE Optional. Target input size. This demo implements
image pre-processing pipeline that is common to human
pose estimation approaches. Image is resize first to
some target size and then the network is reshaped to
fit the input image shape. By default target image
size is determined based on the input shape from IR.
Alternatively it can be manually set via this
parameter. Note that for OpenPose-like nets image is
resized to a predefined height, which is the target
size in this case. For Associative Embedding-like nets
target size is the length of a short image side.
-t PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD
Optional. Probability threshold for poses filtering.
-r, --raw_output_message
Optional. Output inference results raw values showing.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU.
-nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS
Optional. Number of infer requests
-nstreams NUM_STREAMS, --num_streams NUM_STREAMS
Optional. Number of streams to use for inference on
the CPU or/and GPU in throughput mode (for HETERO and
MULTI device cases use format
<device1>:<nstreams1>,<device2>:<nstreams2> or just
<nstreams>)
-nthreads NUM_THREADS, --num_threads NUM_THREADS
Optional. Number of threads to use for inference on
CPU (including HETERO cases)
-loop LOOP, --loop LOOP
Optional. Number of times to repeat the input.
-no_show, --no_show Optional. Don't show output
-u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
Optional. List of monitors to show initially.
デモを実行してみます。
% python3 human_pose_estimation.py -i walking.mov -m ./human-pose-estimation-0001/FP16/human-pose-estimation-0001.xml -at openpose
[ INFO ] Initializing Inference Engine...
[ INFO ] Loading network...
[ INFO ] Using USER_SPECIFIED mode
[ INFO ] Reading network from IR...
[ INFO ] Loading network to plugin...
[ INFO ] Reading network from IR...
[ INFO ] Loading network to plugin...
[ INFO ] Starting inference...
To close the application, press 'CTRL+C' here or switch to the output window and press ESC key
To switch between min_latency/user_specified modes, press TAB key in the output window
[ INFO ]
[ INFO ] Mode: USER_SPECIFIED
[ INFO ] FPS: 10.7
[ INFO ] Latency: 64.1 ms
Integrates the Deep Learning Workbench with the Intel® DevCloud for the Edge as a Beta release. Graphically analyze models using the Deep Learning Workbench on the Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
Introduces support for Red Hat Enterprise Linux (RHEL) 8.2. See System Requirements for more info.
Introduces per-channel quantization support in the Model Optimizer for models quantized with TensorFlow Quantization-Aware Training containing per-channel quantization for weights, which improves performance by model compression and latency reduction.
Pre-trained models and support for public models to streamline development:Public Models: Yolov4 (for object detection), AISpeech (for speech recognition), and DeepLabv3 (for semantic segmentation)
Pre-trained Models: Human Pose Estimation (update), Formula Recognition Polynomial Handwritten (new), Machine Translation (update), Common Sign Language Recognition (New), and Text-to-Speech (new)
New OpenVINO™ Security Add-on, which controls access to model(s) through secure packaging and execution. Based on KVM Virtual machines and Docker* containers and compatible with the OpenVINO™ Model Server, this new add-on enables packaging for flexible deployment and controlled model access.
PyPI project moved from openvino-python to openvino, and 2021.1 version to be removed in the default view. The specific version is still available for users depending on this exact version by using openvino-python==2021.1
