ngraph python sample (#2574)
This sample demonstrates how to execute an inference using ngraph::Function to create a network - added sample - added readme - added lenet weights
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Mikhail Ryzhov
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# nGraph Function Python* Sample {#openvino_inference_engine_samples_ngraph_function_creation_sample_README}
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This sample demonstrates how to execute an inference using ngraph::Function to create a network. The sample uses the LeNet classifications network as an example.
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You do not need an XML file to create a network. The API of ngraph::Function allows to create a network on the fly from the source code. The sample uses one-channel pictures as an input.
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## How It Works
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Upon the start-up, the sample reads command-line parameters and creates a network using the ngraph::Function API and passed weights file.
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Then, the application loads the created network and an image to the Inference Engine core.
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When the inference is done, the application outputs inference results to the standard output stream.
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> **NOTE**: This sample supports models with FP32 weights only.
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The `lenet.bin` weights file was generated by the [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
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tool from the public LeNet model with the `--input_shape [64,1,28,28]` parameter specified.
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The original model is available in the [Caffe* repository](https://github.com/BVLC/caffe/tree/master/examples/mnist) on GitHub\*.
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## Running
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Running the application with the `-h` option yields the following usage message:
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```sh
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./ngraph_function_creation_sample -h
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[ INFO ] InferenceEngine:
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API version ............ <version>
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Build .................. <number>
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Description ....... API
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[ INFO ] Parsing input parameters
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ngraph_function_creation_sample [OPTION]
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Options:
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-h Print a usage message.
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-m "<path>" Path to a .bin file with weights for the trained model
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-i "<path>" Required. Path to an image or folder with images
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-d "<device>" Specify the target device to infer on it. See the list of available devices below. The sample looks for a suitable plugin for the specified device. The default value is CPU.
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-nt "<integer>" Number of top results. The default value is 10.
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Available target devices: <devices>
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```
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For example, to do inference of an UByte image on a GPU run the following command:
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```sh
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./ngraph_function_creation_sample -i <path_to_image> -m <path_to_weights_file> -d GPU
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```
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## Sample Output
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By default, the application outputs top-1 inference result for each inference request.
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## Deprecation Notice
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<table>
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<tr>
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<td><strong>Deprecation Begins</strong></td>
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<td>June 1, 2020</td>
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</tr>
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<tr>
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<td><strong>Removal Date</strong></td>
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<td>December 1, 2020</td>
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</tr>
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</table>
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*Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.*
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*Therefore, ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.*
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## See Also
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* [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
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#!/usr/bin/env python
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"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import sys
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import os
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from argparse import ArgumentParser, SUPPRESS
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import cv2
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import numpy as np
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import logging as log
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from openvino.inference_engine import IECore, IENetwork
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import ngraph
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from ngraph.impl import Function
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from functools import reduce
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import struct as st
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def build_argparser() -> ArgumentParser:
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parser = ArgumentParser(add_help=False)
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args = parser.add_argument_group('Options')
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args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
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args.add_argument('-i', '--input', help='Required. Path to a folder with images or path to an image files',
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required=True,
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type=str, nargs="+")
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args.add_argument('-m', '--model', help='Required. Path to file where weights for the network are located')
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args.add_argument('-d', '--device',
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help='Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: '
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'is acceptable. The sample will look for a suitable plugin for device specified. Default '
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'value is CPU',
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default='CPU', type=str)
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args.add_argument('--labels', help='Optional. Path to a labels mapping file', default=None, type=str)
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args.add_argument('-nt', '--number_top', help='Optional. Number of top results', default=1, type=int)
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return parser
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def list_input_images(input_dirs: list):
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images = []
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for input_dir in input_dirs:
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if os.path.isdir(input_dir):
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for root, directories, filenames in os.walk(input_dir):
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for filename in filenames:
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images.append(os.path.join(root, filename))
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elif os.path.isfile(input_dir):
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images.append(input_dir)
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return images
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def read_image(image_path: np):
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# try to read image in usual image formats
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image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
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# try to open image as ubyte
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if image is None:
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with open(image_path, 'rb') as f:
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image_file = open(image_path, 'rb')
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image_file.seek(0)
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st.unpack('>4B', image_file.read(4)) # need to skip 4 bytes
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nimg = st.unpack('>I', image_file.read(4))[0] # number of images
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nrow = st.unpack('>I', image_file.read(4))[0] # number of rows
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ncolumn = st.unpack('>I', image_file.read(4))[0] # number of column
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nbytes = nimg * nrow * ncolumn * 1 # each pixel data is 1 byte
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assert nimg == 1, log.error('Sample supports ubyte files with 1 image inside')
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image = np.asarray(st.unpack('>' + 'B' * nbytes, image_file.read(nbytes))).reshape(
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(nrow, ncolumn))
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return image
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def shape_and_length(shape: list):
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length = reduce(lambda x, y: x*y, shape)
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return shape, length
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def create_ngraph_function(args) -> Function:
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weights = np.fromfile(args.model, dtype=np.float32)
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weights_offset = 0
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padding_begin = [0, 0]
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padding_end = [0, 0]
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# input
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input_shape = [64, 1, 28, 28]
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param_node = ngraph.parameter(input_shape, np.float32, 'Parameter')
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# convolution 1
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conv_1_kernel_shape, conv_1_kernel_length = shape_and_length([20, 1, 5, 5])
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conv_1_kernel = ngraph.constant(weights[0:conv_1_kernel_length].reshape(conv_1_kernel_shape))
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weights_offset += conv_1_kernel_length
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conv_1_node = ngraph.convolution(param_node, conv_1_kernel, [1, 1], padding_begin, padding_end, [1, 1])
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# add 1
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add_1_kernel_shape, add_1_kernel_length = shape_and_length([1, 20, 1, 1])
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add_1_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + add_1_kernel_length].reshape(add_1_kernel_shape)
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)
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weights_offset += add_1_kernel_length
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add_1_node = ngraph.add(conv_1_node, add_1_kernel)
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# maxpool 1
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maxpool_1_node = ngraph.max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil', None)
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# convolution 2
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conv_2_kernel_shape, conv_2_kernel_length = shape_and_length([50, 20, 5, 5])
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conv_2_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + conv_2_kernel_length].reshape(conv_2_kernel_shape)
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)
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weights_offset += conv_2_kernel_length
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conv_2_node = ngraph.convolution(maxpool_1_node, conv_2_kernel, [1, 1], padding_begin, padding_end, [1, 1])
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# add 2
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add_2_kernel_shape, add_2_kernel_length = shape_and_length([1, 50, 1, 1])
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add_2_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + add_2_kernel_length].reshape(add_2_kernel_shape)
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)
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weights_offset += add_2_kernel_length
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add_2_node = ngraph.add(conv_2_node, add_2_kernel)
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# maxpool 2
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maxpool_2_node = ngraph.max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil', None)
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# reshape 1
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reshape_1_dims, reshape_1_length = shape_and_length([2])
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# workaround to get int64 weights from float32 ndarray w/o unnecessary copying
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dtype_weights = np.frombuffer(
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weights[weights_offset:weights_offset + 2*reshape_1_length], dtype=np.int64
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)
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reshape_1_kernel = ngraph.constant(dtype_weights)
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weights_offset += 2*reshape_1_length
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reshape_1_node = ngraph.reshape(maxpool_2_node, reshape_1_kernel, True)
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# matmul 1
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matmul_1_kernel_shape, matmul_1_kernel_length = shape_and_length([500, 800])
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matmul_1_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + matmul_1_kernel_length].reshape(matmul_1_kernel_shape)
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)
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weights_offset += matmul_1_kernel_length
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matmul_1_node = ngraph.matmul(reshape_1_node, matmul_1_kernel, False, True)
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# add 3
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add_3_kernel_shape, add_3_kernel_length = shape_and_length([1, 500])
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add_3_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + add_3_kernel_length].reshape(add_3_kernel_shape)
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)
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weights_offset += add_3_kernel_length
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add_3_node = ngraph.add(matmul_1_node, add_3_kernel)
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# ReLU
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relu_node = ngraph.relu(add_3_node)
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# reshape 2
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reshape_2_kernel = ngraph.constant(dtype_weights)
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reshape_2_node = ngraph.reshape(relu_node, reshape_2_kernel, True)
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# matmul 2
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matmul_2_kernel_shape, matmul_2_kernel_length = shape_and_length([10, 500])
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matmul_2_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + matmul_2_kernel_length].reshape(matmul_2_kernel_shape)
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)
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weights_offset += matmul_2_kernel_length
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matmul_2_node = ngraph.matmul(reshape_2_node, matmul_2_kernel, False, True)
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# add 4
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add_4_kernel_shape, add_4_kernel_length = shape_and_length([1, 10])
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add_4_kernel = ngraph.constant(
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weights[weights_offset:weights_offset + add_4_kernel_length].reshape(add_4_kernel_shape)
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)
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weights_offset += add_4_kernel_length
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add_4_node = ngraph.add(matmul_2_node, add_4_kernel)
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# softmax
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softmax_axis = 1
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softmax_node = ngraph.softmax(add_4_node, softmax_axis)
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# result
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result_node = ngraph.result(softmax_node)
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# nGraph function
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function = Function(result_node, [param_node], 'lenet')
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return function
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def main():
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log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
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args = build_argparser().parse_args()
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input_images = list_input_images(args.input)
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# Loading network using ngraph function
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ngraph_function = create_ngraph_function(args)
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net = IENetwork(Function.to_capsule(ngraph_function))
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assert len(net.input_info.keys()) == 1, "Sample supports only single input topologies"
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assert len(net.outputs) == 1, "Sample supports only single output topologies"
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log.info("Preparing input blobs")
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input_blob = next(iter(net.input_info))
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out_blob = next(iter(net.outputs))
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net.batch_size = len(input_images)
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# Read and pre-process input images
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n, c, h, w = net.input_info[input_blob].input_data.shape
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images = np.ndarray(shape=(n, c, h, w))
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for i in range(n):
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image = read_image(input_images[i])
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assert image is not None, log.error("Can't open an image {}".format(input_images[i]))
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assert len(image.shape) == 2, log.error('Sample supports images with 1 channel only')
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if image.shape[:] != (w, h):
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log.warning("Image {} is resized from {} to {}".format(input_images[i], image.shape[:], (w, h)))
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image = cv2.resize(image, (w, h))
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images[i] = image
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log.info("Batch size is {}".format(n))
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log.info("Creating Inference Engine")
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ie = IECore()
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log.info('Loading model to the device')
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exec_net = ie.load_network(network=net, device_name=args.device.upper())
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# Start sync inference
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log.info('Creating infer request and starting inference')
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res = exec_net.infer(inputs={input_blob: images})
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# Processing results
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log.info("Processing output blob")
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res = res[out_blob]
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log.info("Top {} results: ".format(args.number_top))
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# Read labels file if it is provided as argument
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labels_map = None
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if args.labels:
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with open(args.labels, 'r') as f:
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labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f]
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classid_str = "classid"
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probability_str = "probability"
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for i, probs in enumerate(res):
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probs = np.squeeze(probs)
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top_ind = np.argsort(probs)[-args.number_top:][::-1]
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print("Image {}\n".format(input_images[i]))
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print(classid_str, probability_str)
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print("{} {}".format('-' * len(classid_str), '-' * len(probability_str)))
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for class_id in top_ind:
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det_label = labels_map[class_id] if labels_map else "{}".format(class_id)
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label_length = len(det_label)
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space_num_before = (len(classid_str) - label_length) // 2
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space_num_after = len(classid_str) - (space_num_before + label_length) + 2
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space_num_before_prob = (len(probability_str) - len(str(probs[class_id]))) // 2
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print("{}{}{}{}{:.7f}".format(' ' * space_num_before, det_label,
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' ' * space_num_after, ' ' * space_num_before_prob,
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probs[class_id]))
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print("\n")
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log.info('This sample is an API example, for any performance measurements '
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'please use the dedicated benchmark_app tool')
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if __name__ == '__main__':
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sys.exit(main() or 0)
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