Я скачал пример python3 для логического вывода DeepLabv3, в котором используется предварительно обученная модель. Время выполнения для фактического вывода составляет около 19 секунд на процессоре, который я использую. Tensorflow был инсталлирован с pip:
pip install intel-tensorflow
Это код из записной книжки Colab Jupyter:
#!/usr/bin/python
import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
from timeit import default_timer as timer
#%tensorflow_version 1.x
import tensorflow.compat.v1 as tf
#import tensorflow as tf
class DeepLabModel(object):
"""Class to load deeplab model and run inference."""
INPUT_TENSOR_NAME = 'ImageTensor:0'
OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
INPUT_SIZE = 513
FROZEN_GRAPH_NAME = 'frozen_inference_graph'
def __init__(self, tarball_path):
"""Creates and loads pretrained deeplab model."""
self.graph = tf.Graph()
graph_def = None
# Extract frozen graph from tar archive.
tar_file = tarfile.open(tarball_path)
for tar_info in tar_file.getmembers():
if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
file_handle = tar_file.extractfile(tar_info)
graph_def = tf.GraphDef.FromString(file_handle.read())
break
tar_file.close()
if graph_def is None:
raise RuntimeError('Cannot find inference graph in tar archive.')
with self.graph.as_default():
tf.import_graph_def(graph_def, name='')
self.sess = tf.Session(graph=self.graph)
def run(self, image):
"""Runs inference on a single image.
Args:
image: A PIL.Image object, raw input image.
Returns:
resized_image: RGB image resized from original input image.
seg_map: Segmentation map of `resized_image`.
"""
width, height = image.size
resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
target_size = (int(resize_ratio * width), int(resize_ratio * height))
resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
start = timer()
batch_seg_map = self.sess.run(
self.OUTPUT_TENSOR_NAME,
feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
end = timer()
print("Inference duration: ", end-start)
seg_map = batch_seg_map[0]
return resized_image, seg_map
def create_pascal_label_colormap():
"""Creates a label colormap used in PASCAL VOC segmentation benchmark.
Returns:
A Colormap for visualizing segmentation results.
"""
colormap = np.zeros((256, 3), dtype=int)
ind = np.arange(256, dtype=int)
for shift in reversed(range(8)):
for channel in range(3):
colormap[:, channel] |= ((ind >> channel) & 1) << shift
ind >>= 3
return colormap
def label_to_color_image(label):
"""Adds color defined by the dataset colormap to the label.
Args:
label: A 2D array with integer type, storing the segmentation label.
Returns:
result: A 2D array with floating type. The element of the array
is the color indexed by the corresponding element in the input label
to the PASCAL color map.
Raises:
ValueError: If label is not of rank 2 or its value is larger than color
map maximum entry.
"""
if label.ndim != 2:
raise ValueError('Expect 2-D input label')
colormap = create_pascal_label_colormap()
if np.max(label) >= len(colormap):
raise ValueError('label value too large.')
return colormap[label]
def vis_segmentation(image, seg_map):
"""Visualizes input image, segmentation map and overlay view."""
plt.figure(figsize=(15, 5))
grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])
plt.subplot(grid_spec[0])
plt.imshow(image)
plt.axis('off')
plt.title('input image')
plt.subplot(grid_spec[1])
seg_image = label_to_color_image(seg_map).astype(np.uint8)
plt.imshow(seg_image)
plt.axis('off')
plt.title('segmentation map')
plt.subplot(grid_spec[2])
plt.imshow(image)
plt.imshow(seg_image, alpha=0.7)
plt.axis('off')
plt.title('segmentation overlay')
unique_labels = np.unique(seg_map)
ax = plt.subplot(grid_spec[3])
plt.imshow(
FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')
ax.yaxis.tick_right()
plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
plt.xticks([], [])
ax.tick_params(width=0.0)
plt.grid('off')
plt.show()
LABEL_NAMES = np.asarray([
'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
])
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
MODEL_NAME = 'xception_coco_voctrainval' # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']
_DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
_MODEL_URLS = {
'mobilenetv2_coco_voctrainaug':
'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
'mobilenetv2_coco_voctrainval':
'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
'xception_coco_voctrainaug':
'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
'xception_coco_voctrainval':
'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
}
_TARBALL_NAME = 'deeplab_model.tar.gz'
model_dir = 'model'
tf.io.gfile.makedirs(model_dir)
download_path = os.path.join(model_dir, _TARBALL_NAME)
print('downloading model, this might take a while...')
urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],
download_path)
print('download completed! loading DeepLab model...')
MODEL = DeepLabModel(download_path)
print('model loaded successfully!')
SAMPLE_IMAGE = 'image1' # @param ['image1', 'image2', 'image3']
IMAGE_URL = 'file:///home/rhobincu/man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg' #@param {type:"string"}
_SAMPLE_URL = ('https://github.com/tensorflow/models/blob/master/research/'
'deeplab/g3doc/img/%s.jpg?raw=true')
def run_visualization(url):
"""Inferences DeepLab model and visualizes result."""
try:
f = urllib.request.urlopen(url)
jpeg_str = f.read()
original_im = Image.open(BytesIO(jpeg_str))
except IOError:
print('Cannot retrieve image. Please check url: ' + url)
return
print('running deeplab on image %s...' % url)
resized_im, seg_map = MODEL.run(original_im)
vis_segmentation(resized_im, seg_map)
image_url = IMAGE_URL or _SAMPLE_URL % SAMPLE_IMAGE
run_visualization(image_url)
С выводом:
rhobincu@ml:~/gitroot/test$ ./test.py
downloading model, this might take a while...
download completed! loading DeepLab model...
2020-04-08 14:51:24.066757: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199980000 Hz
2020-04-08 14:51:24.080415: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5561af0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-08 14:51:24.080567: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-04-08 14:51:24.081792: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
model loaded successfully!
running deeplab on image file:///home/rhobincu/man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg...
Inferrence duration: 18.454864561999784
Я попытался переписать это в Java. Я скомпилировал тензор потока из источников, клонировав тег https://github.com/tensorflow/tensorflow v2.1.0 и выполнив
bazel build -c opt --copt=-mavx --copt=-msse2 --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mfpmath=both //tensorflow:install_headers //tensorflow:libtensorflow_cc.so //tensorflow:libtensorflow_framework.so //tensorflow/java:tensorflow //tensorflow/java:libtensorflow_jni
Ниже приведен соответствующий код Java:
package tensorflowapp;
import java.io.IOException;
import java.io.PrintStream;
import java.nio.ByteBuffer;
import java.nio.charset.Charset;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.tensorflow.DataType;
import org.tensorflow.Graph;
import org.tensorflow.Output;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;
import org.tensorflow.types.UInt8;
/**
* Sample use of the TensorFlow Java API to label images using a pre-trained
* model.
*/
public class LabelImage {
static {
System.load("/usr/local/share/java/opencv4/libopencv_java420.so");
System.load("/opt/tensorflow/java/native/libtensorflow_jni.so");
}
static Session loadDeeplabModel() throws IOException {
Graph graph = new Graph();
graph.importGraphDef(Files.readAllBytes(Paths.get("model/deeplabv3_pascal_trainval/frozen_inference_graph.pb")));
Session session = new Session(graph);
return session;
}
static Tensor<UInt8> matToTensor(Mat image) {
byte[] byteData = new byte[(int) image.total() * image.channels()];
image.get(0, 0, byteData);
return Tensor.create(UInt8.class, new long[]{1, 1, image.width() * image.height(), 3}, ByteBuffer.wrap(byteData));
}
public static void main(String[] args) throws IOException {
Session session = loadDeeplabModel();
Mat image = Imgcodecs.imread(args[0], Imgcodecs.IMREAD_COLOR);
Mat resized = new Mat();
double scale = 513.0 / Math.max(image.width(), image.height());
Size destinationSize = new Size(image.width() * scale, image.height() * scale);
System.out.println("Destination size: " + destinationSize);
Imgproc.resize(image, resized, destinationSize);
Tensor<UInt8> imageTensor = matToTensor(resized);
List<Tensor<?>> result = session.runner().feed("ImageTensor:0", imageTensor).fetch("SemanticPredictions:0").run();//.get(0).expect(Float.class);
System.out.println("Done");
}
}
Выполнение следующей команды:
time java -cp /opt/tensorflow/java/*:dist/TensorFlowApp.jar:/usr/local/share/java/opencv4/opencv-420.jar tensorflowapp.LabelImage ../../man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg
Выводит следующий вывод:
2020-04-08 13:26:14.611201: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199980000 Hz
2020-04-08 13:26:14.626568: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7038dea6d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-08 13:26:14.626612: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Destination size: 513x342
2020-04-08 13:46:59.913359: W tensorflow/core/framework/op_kernel.cc:1655] OP_REQUIRES failed at spacetobatch_op.cc:219 : Invalid argument: padded_shape[1]=21942 is not divisible by block_shape[1]=4
Exception in thread "main" java.lang.IllegalArgumentException: padded_shape[1]=21942 is not divisible by block_shape[1]=4
[[{{node xception_65/exit_flow/block2/unit_1/xception_module/separable_conv1_depthwise/depthwise/SpaceToBatchND}}]]
at org.tensorflow.Session.run(Native Method)
at org.tensorflow.Session.access$100(Session.java:48)
at org.tensorflow.Session$Runner.runHelper(Session.java:326)
at org.tensorflow.Session$Runner.run(Session.java:276)
at tensorflowapp.LabelImage.main(LabelImage.java:58)
Command exited with non-zero status 1
21166.66user 3912.49system 20:48.87elapsed 2008%CPU (0avgtext+0avgdata 27929748maxresident)k
0inputs+408outputs (0major+269297302minor)pagefaults 0swaps
Помимо самой ошибки, время выполнения составляет 3912 секунд ...