IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

rm python style_transfer_sample (#8881)

Browse Source
This commit is contained in:
Vladimir Dudnik
2021-12-05 18:00:11 +03:00
committed by GitHub
parent 89282e3f36
commit 642bd07ffa
4 changed files with 0 additions and 298 deletions
Download Patch File Download Diff File Expand all files Collapse all files

145
samples/python/style_transfer_sample/README.md
View File

@@ -1,145 +0,0 @@
# Style Transfer Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_style_transfer_sample_README}
This sample demonstrates how to do synchronous inference of style transfer networks using Network Batch Size Feature.
You can specify multiple images to input, a network batch size will be set equal to their number automatically.
Models with only 1 input and output are supported.
The following Inference Engine Python API is used in the application:
| Feature | API | Description |
| :----------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------- |
| Network Operations | [IENetwork.batch_size] | Managing of network: configure input and output blobs |
| Custom Extension Kernels | [IECore.add_extension], [IECore.set_config] | Load extension library and config to the device |
Basic Inference Engine API is covered by [Hello Classification Python* Sample](../hello_classification/README.md).
| Options | Values |
| :------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Validated Models | [fast-neural-style-mosaic-onnx](@ref omz_models_model_fast_neural_style_mosaic_onnx) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
## How It Works
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data.
As a result, the program creates an output image(s), logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
Run the application with the `-h` option to see the usage message:
```
python <path_to_sample>/style_transfer_sample.py -h
```
Usage message:
```
usage: style_transfer_sample.py [-h] -m MODEL -i INPUT [INPUT ...]
[-l EXTENSION] [-c CONFIG] [-d DEVICE]
[--original_size] [--mean_val_r MEAN_VAL_R]
[--mean_val_g MEAN_VAL_G]
[--mean_val_b MEAN_VAL_B]
Options:
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml or .onnx file with a trained
model.
-i INPUT [INPUT ...], --input INPUT [INPUT ...]
Required. Path to an image file.
-l EXTENSION, --extension EXTENSION
Optional. Required by the CPU Plugin for executing the
custom operation on a CPU. Absolute path to a shared
library with the kernels implementations.
-c CONFIG, --config CONFIG
Optional. Required by GPU or VPU Plugins for the
custom operation kernel. Absolute path to operation
description file (.xml).
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, MYRIAD, HDDL or HETERO: is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU.
--original_size Optional. Resize an output image to original image
size.
--mean_val_r MEAN_VAL_R
Optional. Mean value of red channel for mean value
subtraction in postprocessing.
--mean_val_g MEAN_VAL_G
Optional. Mean value of green channel for mean value
subtraction in postprocessing.
--mean_val_b MEAN_VAL_B
Optional. Mean value of blue channel for mean value
subtraction in postprocessing.
```
To run the sample, you need specify a model and image:
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
> **NOTES**:
>
> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
>
> - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
### Example
1. Download a pre-trained model using [Model Downloader](@ref omz_tools_downloader):
```
python <path_to_omz_tools>/downloader.py --name fast-neural-style-mosaic-onnx
```
2. `fast-neural-style-mosaic-onnx` model does not need to be converted, because it is already in necessary format, so you can skip this step. If you want to use a other model that is not in the Inference Engine IR or ONNX format, you can convert it using the model converter script:
```
python <path_to_omz_tools>/converter.py --name <model_name>
```
3. Perform inference of `car.bmp` and `cat.jpg` using `fast-neural-style-mosaic-onnx` model on a `GPU`, for example:
```
python <path_to_sample>/style_transfer_sample.py -m <path_to_model>/fast-neural-style-mosaic-onnx.onnx -i <path_to_image>/car.bmp <path_to_image>/cat.jpg -d GPU
```
## Sample Output
The sample application logs each step in a standard output stream and creates an output image (`out_0.bmp`) or a sequence of images (`out_0.bmp`, .., `out_<n>.bmp`) that are redrawn in the style of the style transfer model used.
```
[ INFO ] Creating Inference Engine
[ INFO ] Reading the network: c:\openvino\deployment_tools\open_model_zoo\tools\downloader\public\fast-neural-style-mosaic-onnx\fast-neural-style-mosaic-onnx.onnx
[ INFO ] Configuring input and output blobs
[ INFO ] Loading the model to the plugin
[ WARNING ] Image c:\images\car.bmp is resized from (637, 749) to (224, 224)
[ WARNING ] Image c:\images\cat.jpg is resized from (300, 300) to (224, 224)
[ INFO ] Starting inference in synchronous mode
[ INFO ] Image out_0.bmp created!
[ INFO ] Image out_1.bmp created!
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
```
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
[IECore.add_extension]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#a8a4b671a9928c7c059bd1e76d2333967
[IECore.set_config]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#a2c738cee90fca27146e629825c039a05
[IECore.read_network]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#a0d69c298618fab3a08b855442dca430f
[IENetwork.input_info]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IENetwork.html#data_fields
[IENetwork.outputs]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IENetwork.html#data_fields
[InputInfoPtr.precision]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1InputInfoPtr.html#data_fields
[DataPtr.precision]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1DataPtr.html#data_fields
[IENetwork.batch_size]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IENetwork.html#a79a647cb1b49645616eaeb2ca255ef2e
[IECore.load_network]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#ac9a2e043d14ccfa9c6bbf626cfd69fcc
[InputInfoPtr.input_data.shape]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1InputInfoPtr.html#data_fields
[ExecutableNetwork.infer]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1ExecutableNetwork.html#aea96e8e534c8e23d8b257bad11063519

150
samples/python/style_transfer_sample/style_transfer_sample.py
View File

@@ -1,150 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import logging as log
import os
import sys
import cv2
import numpy as np
from openvino.inference_engine import IECore
def parse_args() -> argparse.Namespace:
"""Parse and return command line arguments"""
parser = argparse.ArgumentParser(add_help=False)
args = parser.add_argument_group('Options')
# fmt: off
args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
args.add_argument('-m', '--model', required=True, type=str,
help='Required. Path to an .xml or .onnx file with a trained model.')
args.add_argument('-i', '--input', required=True, type=str, nargs='+', help='Required. Path to an image file.')
args.add_argument('-l', '--extension', type=str, default=None,
help='Optional. Required by the CPU Plugin for executing the custom operation on a CPU. '
'Absolute path to a shared library with the kernels implementations.')
args.add_argument('-c', '--config', type=str, default=None,
help='Optional. Required by GPU or VPU Plugins for the custom operation kernel. '
'Absolute path to operation description file (.xml).')
args.add_argument('-d', '--device', default='CPU', type=str,
help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
'is acceptable. The sample will look for a suitable plugin for device specified. '
'Default value is CPU.')
args.add_argument('--original_size', action='store_true', default=False,
help='Optional. Resize an output image to original image size.')
args.add_argument('--mean_val_r', default=0, type=float,
help='Optional. Mean value of red channel for mean value subtraction in postprocessing.')
args.add_argument('--mean_val_g', default=0, type=float,
help='Optional. Mean value of green channel for mean value subtraction in postprocessing.')
args.add_argument('--mean_val_b', default=0, type=float,
help='Optional. Mean value of blue channel for mean value subtraction in postprocessing.')
# fmt: on
return parser.parse_args()
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
args = parse_args()
# ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
log.info('Creating Inference Engine')
ie = IECore()
if args.extension and args.device == 'CPU':
log.info(f'Loading the {args.device} extension: {args.extension}')
ie.add_extension(args.extension, args.device)
if args.config and args.device in ('GPU', 'MYRIAD', 'HDDL'):
log.info(f'Loading the {args.device} configuration: {args.config}')
ie.set_config({'CONFIG_FILE': args.config}, args.device)
# ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
log.info(f'Reading the network: {args.model}')
# (.xml and .bin files) or (.onnx file)
net = ie.read_network(model=args.model)
if len(net.input_info) != 1:
log.error('Sample supports only single input topologies')
return -1
if len(net.outputs) != 1:
log.error('Sample supports only single output topologies')
return -1
# ---------------------------Step 3. Configure input & output----------------------------------------------------------
log.info('Configuring input and output blobs')
# Get names of input and output blobs
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
# Set input and output precision manually
net.input_info[input_blob].precision = 'U8'
net.outputs[out_blob].precision = 'FP32'
# Set a batch size to a equal number of input images
net.batch_size = len(args.input)
# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
log.info('Loading the model to the plugin')
exec_net = ie.load_network(network=net, device_name=args.device)
# ---------------------------Step 5. Create infer request--------------------------------------------------------------
# load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
# instance which stores infer requests. So you already created Infer requests in the previous step.
# ---------------------------Step 6. Prepare input---------------------------------------------------------------------
original_images = []
n, c, h, w = net.input_info[input_blob].input_data.shape
input_data = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
original_images.append(image)
if image.shape[:-1] != (h, w):
log.warning(f'Image {args.input[i]} is resized from {image.shape[:-1]} to {(h, w)}')
image = cv2.resize(image, (w, h))
# Change data layout from HWC to CHW
image = image.transpose((2, 0, 1))
input_data[i] = image
# ---------------------------Step 7. Do inference----------------------------------------------------------------------
log.info('Starting inference in synchronous mode')
res = exec_net.infer(inputs={input_blob: input_data})
# ---------------------------Step 8. Process output--------------------------------------------------------------------
res = res[out_blob]
for i in range(n):
output_image = res[i]
# Change data layout from CHW to HWC
output_image = output_image.transpose((1, 2, 0))
# Convert BGR color order to RGB
output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
# Apply mean argument values
output_image = output_image[::] - (args.mean_val_r, args.mean_val_g, args.mean_val_b)
# Set pixel values bitween 0 and 255
output_image = np.clip(output_image, 0, 255)
# Resize a output image to original size
if args.original_size:
h, w, _ = original_images[i].shape
output_image = cv2.resize(output_image, (w, h))
cv2.imwrite(f'out_{i}.bmp', output_image)
if os.path.exists(f'out_{i}.bmp'):
log.info(f'Image out_{i}.bmp created!')
else:
log.error(f'Image out_{i}.bmp was not created. Check your permissions.')
# ----------------------------------------------------------------------------------------------------------------------
log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
return 0
if __name__ == '__main__':
sys.exit(main())