用 java 编写的 Tensorflow 服务客户端没有给出正确的结果
Tensorflow-serving client written in java is not giving correct results
抱歉问了一个很长的问题。但请帮忙。
我在 java 中编写了一个 tensorflow 服务客户端，它请求托管在另一台机器上的 tensorflow 服务器。通信是通过 GRPC 进行的，并且工作正常，即响应请求。然而，随之而来的反应是错误的。该模型的工作是预测客户发送的照片中的人类（戴头盔和不戴头盔）（模型很好）。
所以这个问题可能是由于格式化图像的一些错误引起的，可能是尺寸等。但是我已经尝试了好几天找出所有的小细节，但没有成功。
此外，为此，我也在python中编写了一个客户端，令人惊讶的是它运行良好。服务器的响应是正确的。但我需要在 java 中执行此操作。所以简而言之，我将相同的图像发送到具有 java 和 python 客户端的同一服务器，并得到两个不同的结果。
这里我放了两个客户的代码：
Python-
#PYTHON_CLIENT
from __future__ import print_function
from grpc.beta import implementations
import tensorflow as tf
import glob
import json
from object_detection.utils import visualization_utils as vis_util
from object_detection.utils import plot_util
from object_detection.utils import label_map_util
import object_detection.utils.ops as utils_ops
from PIL import Image
from google.protobuf import json_format as _json_format
import numpy as np
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from object_detection.protos import string_int_label_map_pb2
from object_detection.utils import visualization_utils as vis_util
import cv2
import numpy as np
tf.app.flags.DEFINE_string('server', '<someIPaddress>:9000', 'PredictionService host:port')
tf.app.flags.DEFINE_string('image', './', 'path to image in JPEG format')
FLAGS = tf.app.flags.FLAGS
def out(result):
detection_boxes=[]
detection_scores =[]
detection_classes =[]
db=[]
dc=[]
ds=[]
db.append(result.outputs['detection_boxes'].tensor_shape.dim[0].size)
db.append(result.outputs['detection_boxes'].tensor_shape.dim[1].size)
db.append(result.outputs['detection_boxes'].tensor_shape.dim[2].size)
detection_boxes = np.asarray((result.outputs['detection_boxes'].float_val))
detection_boxes = detection_boxes.reshape([db[0],db[1],db[2]])
print(detection_boxes)
detection_classes = np.asarray((result.outputs['detection_classes'].float_val))
dc.append(result.outputs['detection_classes'].tensor_shape.dim[0].size)
dc.append(result.outputs['detection_classes'].tensor_shape.dim[1].size)
detection_classes = detection_classes.reshape([dc[0],dc[1]])
print(detection_classes)
detection_scores = np.asarray((result.outputs['detection_scores'].float_val))
ds.append(result.outputs['detection_scores'].tensor_shape.dim[0].size)
ds.append(result.outputs['detection_scores'].tensor_shape.dim[1].size)
detection_scores = detection_scores.reshape([dc[0],dc[1]])
print(detection_scores)
return detection_classes,detection_scores,detection_boxes
def main(_):
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
# Create prediction request object
request = predict_pb2.PredictRequest()
request.model_spec.name = 'deeplab'
request.model_spec.signature_name = 'predict_images'
image_data = []
for image in glob.glob(FLAGS.image+'cde.jpg'):
# with open(image, 'rb') as f:
image = cv2.imread(image)
image = image.astype('f')
# image = np.expand_dims(image,0)
image_data.append(image)
# print(cv2.imread(image))
image_data2 = np.asarray(image_data)
# image_data = np.expand_dims(image_data,4)
request.inputs['inputs'].CopyFrom(tf.contrib.util.make_tensor_proto(image_data2, dtype=tf.uint8 ,shape=None))
result = stub.Predict(request, 10.0) # 10 secs timeout
m=[]
n=[]
p=[]
print(result.outputs)
category_index = label_map_util.create_category_index_from_labelmap('/home/<somePathHere>/labels.pbtxt', use_display_name=True)
# Visualization of the results of a detection. # image_data = np.expand_dims(image_data,4)
request.inputs['inputs'].CopyFrom(tf.contrib.util.make_tensor_proto(image_data2, dtype=tf.uint8 ,shape=None))
vis_util.visualize_boxes_and_labels_on_image_array(
image_data,
p,
m,
n,
category_index,
min_score_thresh=.5,
# instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8,
)
if __name__ == '__main__':
tf.app.run()
Java-
//JAVA_CLIENT
public static void main(String[] args) {
String host = "<someIPaddress>";
int port = 9000;
String modelName = "deeplab";
long modelVersion = 1;
// Run predict client to send request
PredictClientt_One client = new PredictClientt_One(host, port);
try {
client.do_predict(modelName, modelVersion);
} catch (Exception e) {
System.out.println(e);
} finally {
try {
client.shutdown();
} catch (Exception e) {
System.out.println(e);
}
}
}
public void shutdown() throws InterruptedException {
channel.shutdown().awaitTermination(5, TimeUnit.SECONDS);
}
public void do_predict(String modelName, long modelVersion) {
// Generate image file to array
int[][][][] featuresTensorData = new int[1][1080][1920][3];
String[] imageFilenames = new String[]{"./cde.jpg"};
for (int i = 0; i < imageFilenames.length; i++) {
// Convert image file to multi-dimension array
File imageFile = new File(imageFilenames[i]);
try {
BufferedImage preImage = ImageIO.read(imageFile);
BufferedImage image = new BufferedImage(preImage.getWidth(), preImage.getHeight(), BufferedImage.TYPE_INT_ARGB); //convert to argb
image.getGraphics().drawImage(preImage, 0, 0, null);
logger.info("Start to convert the image: " + imageFile.getPath());
int imageWidth = 1920;
int imageHeight = 1080;
for (int row = 0; row < imageHeight; row++) {
for (int column = 0; column < imageWidth; column++) {
Color col = new Color (image.getRGB(column, row));
// int red = (pixel >> 16) & 0xff;
// int green = (pixel >> 8) & 0xff;
// int blue = (pixel) & 0xff;
//tried all combination of red, green and blue in [0], [1] and [2]
featuresTensorData[i][row][column][0] = col.getBlue(); //blue;
featuresTensorData[i][row][column][1] = col.getGreen(); //green
featuresTensorData[i][row][column][2] = col.getRed(); //red;
}
}
} catch (IOException e) {
logger.log(Level.WARNING, e.getMessage());
System.exit(1);
}
}
// Generate features TensorProto
TensorProto.Builder featuresTensorBuilder = TensorProto.newBuilder();
for (int i = 0; i < featuresTensorData.length; ++i) {
for (int j = 0; j < featuresTensorData[i].length; ++j) {
for (int k = 0; k < featuresTensorData[i][j].length; ++k) {
for (int l = 0; l < featuresTensorData[i][j][k].length; ++l) {
featuresTensorBuilder.addFloatVal(featuresTensorData[i][j][k][l]);
}
}
}
}
TensorShapeProto.Dim featuresDim1 = TensorShapeProto.Dim.newBuilder().setSize(1).build();
TensorShapeProto.Dim featuresDim2 = TensorShapeProto.Dim.newBuilder().setSize(1080).build();
TensorShapeProto.Dim featuresDim3 = TensorShapeProto.Dim.newBuilder().setSize(1920).build();
TensorShapeProto.Dim featuresDim4 = TensorShapeProto.Dim.newBuilder().setSize(3).build();
TensorShapeProto featuresShape = TensorShapeProto.newBuilder().addDim(featuresDim1).addDim(featuresDim2).addDim(featuresDim3).addDim(featuresDim4).build();
featuresTensorBuilder.setDtype(org.tensorflow.framework.DataType.DT_UINT8).setTensorShape(featuresShape);
TensorProto featuresTensorProto = featuresTensorBuilder.build();
// Generate gRPC request
com.google.protobuf.Int64Value version = com.google.protobuf.Int64Value.newBuilder().setValue(modelVersion).build();
Model.ModelSpec modelSpec = Model.ModelSpec.newBuilder().setName(modelName).setVersion(version).build();
Predict.PredictRequest request = Predict.PredictRequest.newBuilder().setModelSpec(modelSpec).putInputs("inputs", featuresTensorProto).build();
// Request gRPC server
Predict.PredictResponse response;
try {
response = blockingStub.predict(request);
java.util.Map<java.lang.String, org.tensorflow.framework.TensorProto> outputs = response.getOutputsMap();
for (java.util.Map.Entry<java.lang.String, org.tensorflow.framework.TensorProto> entry : outputs.entrySet()) {
System.out.println("Key: " + entry.getKey() + ",\nValue: " + entry.getValue());
}
} catch (StatusRuntimeException e) {
logger.log(Level.WARNING, "RPC failed: {0}", e.getStatus());
return;
}
}
来自服务器的响应以包含四个键值对的哈希映射（或字典）的形式出现：
{
'detection_scores': <some value>,
'detection_boxes': <some value>,
'detection_classes': <some value>,
'num_detections': <some value>
}
python 的 'detection_scores' 的值如下：0.9.., 0.8..., 0.7..., 0.1..., 0.04...（所以 3检测到人类）。
而 java 的 'detection_scores' 的值从：0.005..（在同一张照片中）开始。此外，所有的边界框也都放在照片的最左边，python的边界框在人脸上。
请帮忙。并感谢您的耐心阅读！
我正在回答我自己的问题，因为我刚刚想出了解决方案。
我需要解决的问题是将 addFloatVal() 更改为 addIntVal()。
这里：
TensorProto.Builder featuresTensorBuilder = TensorProto.newBuilder();
for (int i = 0; i < featuresTensorData.length; ++i) {
for (int j = 0; j < featuresTensorData[i].length; ++j) {
for (int k = 0; k < featuresTensorData[i][j].length; ++k) {
for (int l = 0; l < featuresTensorData[i][j][k].length; ++l) {
featuresTensorBuilder.addFloatVal(featuresTensorData[i][j][k][l]); //In this line
}
}
}
}
这么小的修复，我已经浪费了整整 2 天的时间来做我能做的一切！伤心。
抱歉问了一个很长的问题。但请帮忙。
我在 java 中编写了一个 tensorflow 服务客户端，它请求托管在另一台机器上的 tensorflow 服务器。通信是通过 GRPC 进行的，并且工作正常，即响应请求。然而，随之而来的反应是错误的。该模型的工作是预测客户发送的照片中的人类（戴头盔和不戴头盔）（模型很好）。
所以这个问题可能是由于格式化图像的一些错误引起的，可能是尺寸等。但是我已经尝试了好几天找出所有的小细节，但没有成功。
此外，为此，我也在python中编写了一个客户端，令人惊讶的是它运行良好。服务器的响应是正确的。但我需要在 java 中执行此操作。所以简而言之，我将相同的图像发送到具有 java 和 python 客户端的同一服务器，并得到两个不同的结果。
这里我放了两个客户的代码：
Python-
#PYTHON_CLIENT
from __future__ import print_function
from grpc.beta import implementations
import tensorflow as tf
import glob
import json
from object_detection.utils import visualization_utils as vis_util
from object_detection.utils import plot_util
from object_detection.utils import label_map_util
import object_detection.utils.ops as utils_ops
from PIL import Image
from google.protobuf import json_format as _json_format
import numpy as np
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from object_detection.protos import string_int_label_map_pb2
from object_detection.utils import visualization_utils as vis_util
import cv2
import numpy as np
tf.app.flags.DEFINE_string('server', '<someIPaddress>:9000', 'PredictionService host:port')
tf.app.flags.DEFINE_string('image', './', 'path to image in JPEG format')
FLAGS = tf.app.flags.FLAGS
def out(result):
detection_boxes=[]
detection_scores =[]
detection_classes =[]
db=[]
dc=[]
ds=[]
db.append(result.outputs['detection_boxes'].tensor_shape.dim[0].size)
db.append(result.outputs['detection_boxes'].tensor_shape.dim[1].size)
db.append(result.outputs['detection_boxes'].tensor_shape.dim[2].size)
detection_boxes = np.asarray((result.outputs['detection_boxes'].float_val))
detection_boxes = detection_boxes.reshape([db[0],db[1],db[2]])
print(detection_boxes)
detection_classes = np.asarray((result.outputs['detection_classes'].float_val))
dc.append(result.outputs['detection_classes'].tensor_shape.dim[0].size)
dc.append(result.outputs['detection_classes'].tensor_shape.dim[1].size)
detection_classes = detection_classes.reshape([dc[0],dc[1]])
print(detection_classes)
detection_scores = np.asarray((result.outputs['detection_scores'].float_val))
ds.append(result.outputs['detection_scores'].tensor_shape.dim[0].size)
ds.append(result.outputs['detection_scores'].tensor_shape.dim[1].size)
detection_scores = detection_scores.reshape([dc[0],dc[1]])
print(detection_scores)
return detection_classes,detection_scores,detection_boxes
def main(_):
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
# Create prediction request object
request = predict_pb2.PredictRequest()
request.model_spec.name = 'deeplab'
request.model_spec.signature_name = 'predict_images'
image_data = []
for image in glob.glob(FLAGS.image+'cde.jpg'):
# with open(image, 'rb') as f:
image = cv2.imread(image)
image = image.astype('f')
# image = np.expand_dims(image,0)
image_data.append(image)
# print(cv2.imread(image))
image_data2 = np.asarray(image_data)
# image_data = np.expand_dims(image_data,4)
request.inputs['inputs'].CopyFrom(tf.contrib.util.make_tensor_proto(image_data2, dtype=tf.uint8 ,shape=None))
result = stub.Predict(request, 10.0) # 10 secs timeout
m=[]
n=[]
p=[]
print(result.outputs)
category_index = label_map_util.create_category_index_from_labelmap('/home/<somePathHere>/labels.pbtxt', use_display_name=True)
# Visualization of the results of a detection. # image_data = np.expand_dims(image_data,4)
request.inputs['inputs'].CopyFrom(tf.contrib.util.make_tensor_proto(image_data2, dtype=tf.uint8 ,shape=None))
vis_util.visualize_boxes_and_labels_on_image_array(
image_data,
p,
m,
n,
category_index,
min_score_thresh=.5,
# instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8,
)
if __name__ == '__main__':
tf.app.run()
Java-
//JAVA_CLIENT
public static void main(String[] args) {
String host = "<someIPaddress>";
int port = 9000;
String modelName = "deeplab";
long modelVersion = 1;
// Run predict client to send request
PredictClientt_One client = new PredictClientt_One(host, port);
try {
client.do_predict(modelName, modelVersion);
} catch (Exception e) {
System.out.println(e);
} finally {
try {
client.shutdown();
} catch (Exception e) {
System.out.println(e);
}
}
}
public void shutdown() throws InterruptedException {
channel.shutdown().awaitTermination(5, TimeUnit.SECONDS);
}
public void do_predict(String modelName, long modelVersion) {
// Generate image file to array
int[][][][] featuresTensorData = new int[1][1080][1920][3];
String[] imageFilenames = new String[]{"./cde.jpg"};
for (int i = 0; i < imageFilenames.length; i++) {
// Convert image file to multi-dimension array
File imageFile = new File(imageFilenames[i]);
try {
BufferedImage preImage = ImageIO.read(imageFile);
BufferedImage image = new BufferedImage(preImage.getWidth(), preImage.getHeight(), BufferedImage.TYPE_INT_ARGB); //convert to argb
image.getGraphics().drawImage(preImage, 0, 0, null);
logger.info("Start to convert the image: " + imageFile.getPath());
int imageWidth = 1920;
int imageHeight = 1080;
for (int row = 0; row < imageHeight; row++) {
for (int column = 0; column < imageWidth; column++) {
Color col = new Color (image.getRGB(column, row));
// int red = (pixel >> 16) & 0xff;
// int green = (pixel >> 8) & 0xff;
// int blue = (pixel) & 0xff;
//tried all combination of red, green and blue in [0], [1] and [2]
featuresTensorData[i][row][column][0] = col.getBlue(); //blue;
featuresTensorData[i][row][column][1] = col.getGreen(); //green
featuresTensorData[i][row][column][2] = col.getRed(); //red;
}
}
} catch (IOException e) {
logger.log(Level.WARNING, e.getMessage());
System.exit(1);
}
}
// Generate features TensorProto
TensorProto.Builder featuresTensorBuilder = TensorProto.newBuilder();
for (int i = 0; i < featuresTensorData.length; ++i) {
for (int j = 0; j < featuresTensorData[i].length; ++j) {
for (int k = 0; k < featuresTensorData[i][j].length; ++k) {
for (int l = 0; l < featuresTensorData[i][j][k].length; ++l) {
featuresTensorBuilder.addFloatVal(featuresTensorData[i][j][k][l]);
}
}
}
}
TensorShapeProto.Dim featuresDim1 = TensorShapeProto.Dim.newBuilder().setSize(1).build();
TensorShapeProto.Dim featuresDim2 = TensorShapeProto.Dim.newBuilder().setSize(1080).build();
TensorShapeProto.Dim featuresDim3 = TensorShapeProto.Dim.newBuilder().setSize(1920).build();
TensorShapeProto.Dim featuresDim4 = TensorShapeProto.Dim.newBuilder().setSize(3).build();
TensorShapeProto featuresShape = TensorShapeProto.newBuilder().addDim(featuresDim1).addDim(featuresDim2).addDim(featuresDim3).addDim(featuresDim4).build();
featuresTensorBuilder.setDtype(org.tensorflow.framework.DataType.DT_UINT8).setTensorShape(featuresShape);
TensorProto featuresTensorProto = featuresTensorBuilder.build();
// Generate gRPC request
com.google.protobuf.Int64Value version = com.google.protobuf.Int64Value.newBuilder().setValue(modelVersion).build();
Model.ModelSpec modelSpec = Model.ModelSpec.newBuilder().setName(modelName).setVersion(version).build();
Predict.PredictRequest request = Predict.PredictRequest.newBuilder().setModelSpec(modelSpec).putInputs("inputs", featuresTensorProto).build();
// Request gRPC server
Predict.PredictResponse response;
try {
response = blockingStub.predict(request);
java.util.Map<java.lang.String, org.tensorflow.framework.TensorProto> outputs = response.getOutputsMap();
for (java.util.Map.Entry<java.lang.String, org.tensorflow.framework.TensorProto> entry : outputs.entrySet()) {
System.out.println("Key: " + entry.getKey() + ",\nValue: " + entry.getValue());
}
} catch (StatusRuntimeException e) {
logger.log(Level.WARNING, "RPC failed: {0}", e.getStatus());
return;
}
}
来自服务器的响应以包含四个键值对的哈希映射（或字典）的形式出现：
{
'detection_scores': <some value>,
'detection_boxes': <some value>,
'detection_classes': <some value>,
'num_detections': <some value>
}
python 的 'detection_scores' 的值如下：0.9.., 0.8..., 0.7..., 0.1..., 0.04...（所以 3检测到人类）。
而 java 的 'detection_scores' 的值从：0.005..（在同一张照片中）开始。此外，所有的边界框也都放在照片的最左边，python的边界框在人脸上。
请帮忙。并感谢您的耐心阅读！
我正在回答我自己的问题，因为我刚刚想出了解决方案。
我需要解决的问题是将 addFloatVal() 更改为 addIntVal()。
这里：
TensorProto.Builder featuresTensorBuilder = TensorProto.newBuilder();
for (int i = 0; i < featuresTensorData.length; ++i) {
for (int j = 0; j < featuresTensorData[i].length; ++j) {
for (int k = 0; k < featuresTensorData[i][j].length; ++k) {
for (int l = 0; l < featuresTensorData[i][j][k].length; ++l) {
featuresTensorBuilder.addFloatVal(featuresTensorData[i][j][k][l]); //In this line
}
}
}
}
这么小的修复，我已经浪费了整整 2 天的时间来做我能做的一切！伤心。