I'm trying below but not work.
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
using namespace tensorflow;
REGISTER_OP("Auc")
.Input("predicts: T1")
.Input("labels: T2")
.Output("z: double")
.Attr("T1: {float, double}")
.Attr("T2: {int32, int64}")
.SetIsCommutative()
.Doc(R"doc(
Given preidicts and labels output it's auc
)doc");
class AucOp : public OpKernel {
public:
explicit AucOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& predicts_tensor = context->input(0);
const Tensor& labels_tensor = context->input(1);
auto predicts = predicts_tensor.flat<double>();
auto labels = labels_tensor.flat<int32>();
// Create an output tensor
Tensor* output_tensor = NULL;
TensorShape output_shape;
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output_tensor));
output_tensor->flat<double>().setConstant(predicts(0) * labels(0));
}
};
REGISTER_KERNEL_BUILDER(Name("Auc").Device(DEVICE_CPU), AucOp);
test.py
predicts = tf.constant([0.8, 0.5, 0.12])
labels = tf.constant([-1, 1, 1])
output = tf.user_ops.auc(predicts, labels)
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
print output.eval()
./test.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 8
I tensorflow/core/common_runtime/direct_session.cc:60] Direct session inter op parallelism threads: 8
F ./tensorflow/core/public/tensor.h:453] Check failed: dtype() == DataTypeToEnum::v() (1 vs. 2)
Aborted
The issue is that the predicts tensor in your Python program has type float, and your op registration accepts this as a valid type for the predicts input (since T1 can be float or double), but AucOp::Compute() assumes that the predicts input always has type double (in the call to predicts_tensor.flat<double>()). The tensorflow::Tensor class does not convert the type of elements in the tensor when you ask for values of a different type, and instead it raises a fatal error.
There are several possible solutions:
To get things working quickly, you could change the type of predicts in your Python program to tf.float64 (which is a synonym for double in the Python front-end):
predicts = tf.constant([0.8, 0.5, 0.12], dtype=tf.float64)
You could start by defining a simpler op that accepts inputs of a single type only:
REGISTER_OP("Auc")
.Input("predicts: double")
.Input("labels: int32")
...;
You could add code in the AucOp::Compute() method to test the input type and access the input values as appropriate. (Use this->input_type(i) to find the type of the ith input.
You could define a templated class AucOp<TPredict, TLabel>, then use TypeConstraint<> in the REGISTER_KERNEL_BUILDER call to define specializations for each of the four valid combinations of prediction and label types. This would look something like:
REGISTER_KERNEL_BUILDER(Name("Auc")
.Device(DEVICE_CPU)
.TypeConstraint<float>("T1")
.TypeConstraint<int32>("T2"),
AucOp<float, int32>);
// etc. for AucOp<double, int32>, AucOp<float, int64>, and AucOp<double, int64>.
Related
I'm trying to save my model so that when called from tf-serving the output is:
{
"results": [
{ "label1": x.xxxxx, "label2": x.xxxxx },
{ "label1": x.xxxxx, "label2": x.xxxxx }
]
}
where label1 and label2 are my labels and x.xxxxx are the probability of that label.
This is what I'm trying:
class TFModel(tf.Module):
def __init__(self, model: tf.keras.Model) -> None:
self.labels = ['label1', 'label2']
self.model = model
#tf.function(input_signature=[tf.TensorSpec(shape=(1, ), dtype=tf.string)])
def prediction(self, pagetext: str):
return
{ 'results': tf.constant([{k: v for dct in [{self.labels[c]: f"{x:.5f}"} for (c,x) in enumerate(results[i])] for k, v in dct.items()}
for i in range(len(results.numpy()))])}
# and then save it:
tf_model_wrapper = TFModel(classifier_model)
tf.saved_model.save(tf_model_wrapper.model,
saved_model_path,
signatures={'serving_default':tf_model_wrapper.prediction}
)
Side Note: Apparently in TensorFlow v2.0 if signatures is omitted it should scan the object for the first #tf.function (according to this: https://www.tensorflow.org/api_docs/python/tf/saved_model/save) but in reality that doesn't seem to work. Instead, the model saves successfully with no errors and the #tf.function is not called, but default output is returned instead.
The error I get from the above is:
ValueError: Got a non-Tensor value <tf.Operation 'PartitionedCall' type=PartitionedCall> for key 'output_0' in the output of the function __inference_prediction_125493 used to generate the SavedModel signature 'serving_default'. Outputs for functions used as signatures must be a single Tensor, a sequence of Tensors, or a dictionary from string to Tensor.
I wrapped the result in tf.constant above because of this error, thinking it might be a quick fix, but I think it's me just being naive and not understanding Tensors properly.
I tried a bunch of other things before learning that [all outputs must be return values].1
How can I change the output to be as I want it to be?
You can see a Tensor as a multidimensional vector, i.e a structure with a fixed size and dimension and containing elements sharing the same type. Your return value is a map between a string and a list of dictionaries. A list of dictionaries cannot be converted to a tensor, because there is no guarantee that the number of dimensions and their size is constant, nor a guarantee that each element is sharing the same type.
You could instead return the raw output of your network, which should be a tensor and do your post processing outside of tensorflow-serving.
If you really want to do something like in your question, you can use a Tensor of strings instead, and you could use some code like that:
labels = tf.constant(['label1', 'label2'])
# if your batch size is dynamic, you can use tf.shape on your results variable to find it at runtime
batch_size = 32
# assuming your model returns something with the shape (N,2)
results = tf.random.uniform((batch_size,2))
res_as_str = tf.strings.as_string(results, precision=5)
return {
"results": tf.stack(
[tf.tile(labels[None, :], [batch_size, 1]), res_as_str], axis=-1
)
}
The output will be a dictionary mapping the value "results" to a Tensor of dimensions (Batch, number of labels, 2), the last dimension containing the label name and its corresponding value.
I'm currently working on a Tensorflow Lite image classifier app that can recognice UNO cards. But when I'm running the float model in the class ImageClassifier, something is wrong.
The error is the next one:
java.lang.IllegalArgumentException: Cannot copy from a TensorFlowLite tensor (Identity) with shape [1, 10647, 4] to a Java object with shape [1, 15].
Here's the code that throw that error:
tflite.run(imgData, labelProbArray);
And this is how I have created imgData and labelProbArray:
private static final int DIM_BATCH_SIZE = 1;
private static final int DIM_PIXEL_SIZE = 3; //r+g+b = 1+1+1
static final int DIM_IMG_SIZE_X = 416;
static final int DIM_IMG_SIZE_Y = 416;
imgData = ByteBuffer.allocateDirect(DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE * 4); //The last value because size of float is 4
labelProbArray = new float[1][labelList.size()]; // {1, 15}
You can chech the inputs and ouputs of the .tflite file. Source.
I know you should create a buffer for the output values, but I tried to import this and didn't work:
import org.tensorflow.lite.support.tensorbuffer.TensorBuffer;
Any ideas? Thank you so much for read me^^
Edit v2:
Thanks to yyoon I realise that I didn't populate my model with metadata, so I run this line in my cmd:
python ./metadata_writer_for_image_classifier_uno.py \ --model_file=./model_without_metadata/custom.tflite \ --label_file=./model_without_metadata/labels.txt \ --export_directory=model_with_metadata
Before that, I modified this file with my data:
_MODEL_INFO = {
"custom.tflite":
ModelSpecificInfo(
name="UNO image classifier",
version="v1",
image_width=416,
image_height=416,
image_min=0,
image_max=255,
mean=[127.5],
std=[127.5],
num_classes=15)
}
And another error appeared:
ValueError: The number of output tensors (2) should match the number of output tensor metadata (1)
Idk why my model have 2 tensors outputs...
I have save the model using tf.estimator .method export_savedmodel as follows:
export_dir="exportModel/"
feature_spec = tf.feature_column.make_parse_example_spec(feature_columns)
input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
classifier.export_savedmodel(export_dir, input_receiver_fn, as_text=False, checkpoint_path="Model/model.ckpt-400")
How can I import this saved model and use for predictions?
I tried to search for a good base example, but it appears the documentation and samples are a bit scattered for this topic. So let's start with a base example: the tf.estimator quickstart.
That particular example doesn't actually export a model, so let's do that (not need for use case 1):
def serving_input_receiver_fn():
"""Build the serving inputs."""
# The outer dimension (None) allows us to batch up inputs for
# efficiency. However, it also means that if we want a prediction
# for a single instance, we'll need to wrap it in an outer list.
inputs = {"x": tf.placeholder(shape=[None, 4], dtype=tf.float32)}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
export_dir = classifier.export_savedmodel(
export_dir_base="/path/to/model",
serving_input_receiver_fn=serving_input_receiver_fn)
Huge asterisk on this code: there appears to be a bug in TensorFlow 1.3 that doesn't allow you to do the above export on a "canned" estimator (such as DNNClassifier). For a workaround, see the "Appendix: Workaround" section.
The code below references export_dir (return value from the export step) to emphasize that it is not "/path/to/model", but rather, a subdirectory of that directory whose name is a timestamp.
Use Case 1: Perform prediction in the same process as training
This is an sci-kit learn type of experience, and is already exemplified by the sample. For completeness' sake, you simply call predict on the trained model:
classifier.train(input_fn=train_input_fn, steps=2000)
# [...snip...]
predictions = list(classifier.predict(input_fn=predict_input_fn))
predicted_classes = [p["classes"] for p in predictions]
Use Case 2: Load a SavedModel into Python/Java/C++ and perform predictions
Python Client
Perhaps the easiest thing to use if you want to do prediction in Python is SavedModelPredictor. In the Python program that will use the SavedModel, we need code like this:
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model(export_dir)
predictions = predict_fn(
{"x": [[6.4, 3.2, 4.5, 1.5],
[5.8, 3.1, 5.0, 1.7]]})
print(predictions['scores'])
Java Client
package dummy;
import java.nio.FloatBuffer;
import java.util.Arrays;
import java.util.List;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
public class Client {
public static void main(String[] args) {
Session session = SavedModelBundle.load(args[0], "serve").session();
Tensor x =
Tensor.create(
new long[] {2, 4},
FloatBuffer.wrap(
new float[] {
6.4f, 3.2f, 4.5f, 1.5f,
5.8f, 3.1f, 5.0f, 1.7f
}));
// Doesn't look like Java has a good way to convert the
// input/output name ("x", "scores") to their underlying tensor,
// so we hard code them ("Placeholder:0", ...).
// You can inspect them on the command-line with saved_model_cli:
//
// $ saved_model_cli show --dir $EXPORT_DIR --tag_set serve --signature_def serving_default
final String xName = "Placeholder:0";
final String scoresName = "dnn/head/predictions/probabilities:0";
List<Tensor> outputs = session.runner()
.feed(xName, x)
.fetch(scoresName)
.run();
// Outer dimension is batch size; inner dimension is number of classes
float[][] scores = new float[2][3];
outputs.get(0).copyTo(scores);
System.out.println(Arrays.deepToString(scores));
}
}
C++ Client
You'll likely want to use tensorflow::LoadSavedModel with Session.
#include <unordered_set>
#include <utility>
#include <vector>
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/public/session.h"
namespace tf = tensorflow;
int main(int argc, char** argv) {
const string export_dir = argv[1];
tf::SavedModelBundle bundle;
tf::Status load_status = tf::LoadSavedModel(
tf::SessionOptions(), tf::RunOptions(), export_dir, {"serve"}, &bundle);
if (!load_status.ok()) {
std::cout << "Error loading model: " << load_status << std::endl;
return -1;
}
// We should get the signature out of MetaGraphDef, but that's a bit
// involved. We'll take a shortcut like we did in the Java example.
const string x_name = "Placeholder:0";
const string scores_name = "dnn/head/predictions/probabilities:0";
auto x = tf::Tensor(tf::DT_FLOAT, tf::TensorShape({2, 4}));
auto matrix = x.matrix<float>();
matrix(0, 0) = 6.4;
matrix(0, 1) = 3.2;
matrix(0, 2) = 4.5;
matrix(0, 3) = 1.5;
matrix(0, 1) = 5.8;
matrix(0, 2) = 3.1;
matrix(0, 3) = 5.0;
matrix(0, 4) = 1.7;
std::vector<std::pair<string, tf::Tensor>> inputs = {{x_name, x}};
std::vector<tf::Tensor> outputs;
tf::Status run_status =
bundle.session->Run(inputs, {scores_name}, {}, &outputs);
if (!run_status.ok()) {
cout << "Error running session: " << run_status << std::endl;
return -1;
}
for (const auto& tensor : outputs) {
std::cout << tensor.matrix<float>() << std::endl;
}
}
Use Case 3: Serve a model using TensorFlow Serving
Exporting models in a manner amenable to serving a Classification model requires that the input be a tf.Example object. Here's how we might export a model for TensorFlow serving:
def serving_input_receiver_fn():
"""Build the serving inputs."""
# The outer dimension (None) allows us to batch up inputs for
# efficiency. However, it also means that if we want a prediction
# for a single instance, we'll need to wrap it in an outer list.
example_bytestring = tf.placeholder(
shape=[None],
dtype=tf.string,
)
features = tf.parse_example(
example_bytestring,
tf.feature_column.make_parse_example_spec(feature_columns)
)
return tf.estimator.export.ServingInputReceiver(
features, {'examples': example_bytestring})
export_dir = classifier.export_savedmodel(
export_dir_base="/path/to/model",
serving_input_receiver_fn=serving_input_receiver_fn)
The reader is referred to TensorFlow Serving's documentation for more instructions on how to setup TensorFlow Serving, so I'll only provide the client code here:
# Omitting a bunch of connection/initialization code...
# But at some point we end up with a stub whose lifecycle
# is generally longer than that of a single request.
stub = create_stub(...)
# The actual values for prediction. We have two examples in this
# case, each consisting of a single, multi-dimensional feature `x`.
# This data here is the equivalent of the map passed to the
# `predict_fn` in use case #2.
examples = [
tf.train.Example(
features=tf.train.Features(
feature={"x": tf.train.Feature(
float_list=tf.train.FloatList(value=[6.4, 3.2, 4.5, 1.5]))})),
tf.train.Example(
features=tf.train.Features(
feature={"x": tf.train.Feature(
float_list=tf.train.FloatList(value=[5.8, 3.1, 5.0, 1.7]))})),
]
# Build the RPC request.
predict_request = predict_pb2.PredictRequest()
predict_request.model_spec.name = "default"
predict_request.inputs["examples"].CopyFrom(
tensor_util.make_tensor_proto(examples, tf.float32))
# Perform the actual prediction.
stub.Predict(request, PREDICT_DEADLINE_SECS)
Note that the key, examples, that is referenced in the predict_request.inputs needs to match the key used in the serving_input_receiver_fn at export time (cf. the constructor to ServingInputReceiver in that code).
Appendix: Working around Exports from Canned Models in TF 1.3
There appears to be a bug in TensorFlow 1.3 in which canned models do not export properly for use case 2 (the problem does not exist for "custom" estimators). Here's is a workaround that wraps a DNNClassifier to make things work, specifically for the Iris example:
# Build 3 layer DNN with 10, 20, 10 units respectively.
class Wrapper(tf.estimator.Estimator):
def __init__(self, **kwargs):
dnn = tf.estimator.DNNClassifier(**kwargs)
def model_fn(mode, features, labels):
spec = dnn._call_model_fn(features, labels, mode)
export_outputs = None
if spec.export_outputs:
export_outputs = {
"serving_default": tf.estimator.export.PredictOutput(
{"scores": spec.export_outputs["serving_default"].scores,
"classes": spec.export_outputs["serving_default"].classes})}
# Replace the 3rd argument (export_outputs)
copy = list(spec)
copy[4] = export_outputs
return tf.estimator.EstimatorSpec(mode, *copy)
super(Wrapper, self).__init__(model_fn, kwargs["model_dir"], dnn.config)
classifier = Wrapper(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
I dont think there is a bug with canned Estimators (or rather if there was ever one, it has been fixed). I was able to successfully export a canned estimator model using Python and import it in Java.
Here is my code to export the model:
a = tf.feature_column.numeric_column("a");
b = tf.feature_column.numeric_column("b");
feature_columns = [a, b];
model = tf.estimator.DNNClassifier(feature_columns=feature_columns ...);
# To export
feature_spec = tf.feature_column.make_parse_example_spec(feature_columns);
export_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec);
servable_model_path = model.export_savedmodel(servable_model_dir, export_input_fn, as_text=True);
To import the model in Java, I used the Java client code provided by rhaertel80 above and it works. Hope this also answers Ben Fowler's question above.
It appears that the TensorFlow team does not agree that there is a bug in version 1.3 using canned estimators for exporting a model under use case #2. I submitted a bug report here:
https://github.com/tensorflow/tensorflow/issues/13477
The response I received from TensorFlow is that the input must only be a single string tensor. It appears that there may be a way to consolidate multiple features into a single string tensor using serialized TF.examples, but I have not found a clear method to do this. If anyone has code showing how to do this, I would be appreciative.
You need to export the saved model using tf.contrib.export_savedmodel and you need to define input receiver function to pass input to.
Later you can load the saved model ( generally saved.model.pb) from the disk and serve it.
TensorFlow: How to predict from a SavedModel?
The following code uses a tf.while_loop(...) for computations of a dynamic length.
outputs_tensor_array = tf.TensorArray(tf.float32,
size=0,
clear_after_read=False,
infer_shape=False,
dynamic_size = True,
element_shape[self.batch_size, self.size])
initial_args = [outputs_tensor_array, 0]
outputs, *_ = tf.while_loop(lambda out, idx, *_ : idx < max_len,
func,
initial_args + additional_args,
parallel_iterations = 32,
swap_memory = True)
outputs = outputs.stack()
I'm wondering if its possible to enforce a size, or atleast make that size be None in order to enforce a size constraint and enable further computations down the graph. The current shape is [?, batch, hidden_size]
tensor.set_shape will refine the static shape information and throw an error if it is incompatible with current static shape information (in the TensorArray.stack() case it will let you set any value for the zeroth dimension's static shape information).
tf.reshape can also be useful for asserting/filling in shape information, although it's not perfect. It will only throw an error if the size of the Tensor is wrong when the graph is executed (and may otherwise hide a shape error downstream).
More complicated, but you can also set_shape for the static shape information and then use tf.Assert with tf.shape to check the Tensor's shape when the graph is executed.
I am running the sample iris program in TensorFlow Serving. Since it is a TF.Learn model, I am exporting the model using the following classifier.export(export_dir=model_dir,signature_fn=my_classification_signature_fn) and the signature_fn is defined as shown below:
def my_classification_signature_fn(examples, unused_features, predictions):
"""Creates classification signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor` or dict of tensors that contains the classes tensor
as in {'classes': `Tensor`}.
Returns:
Tuple of default classification signature and empty named signatures.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None when using this signature fn.')
if isinstance(predictions, dict):
default_signature = exporter.classification_signature(
examples, classes_tensor=predictions['classes'])
else:
default_signature = exporter.classification_signature(
examples, classes_tensor=predictions)
named_graph_signatures={
'inputs': exporter.generic_signature({'x_values': examples}),
'outputs': exporter.generic_signature({'preds': predictions})}
return default_signature, named_graph_signatures
The model gets successfully exported using the following piece of code.
I have created a client which makes real-time predictions using TensorFlow Serving.
The following is the code for the client:
flags.DEFINE_string("model_dir", "/tmp/iris_model_dir", "Base directory for output models.")
tf.app.flags.DEFINE_integer('concurrency', 1,
'maximum number of concurrent inference requests')
tf.app.flags.DEFINE_string('server', '', 'PredictionService host:port')
#connection
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
# Classify two new flower samples.
new_samples = np.array([5.8, 3.1, 5.0, 1.7], dtype=float)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'iris'
request.inputs["x_values"].CopyFrom(
tf.contrib.util.make_tensor_proto(new_samples))
result = stub.Predict(request, 10.0) # 10 secs timeout
However, on making the predictions, the following error is displayed:
grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="Output 0 of type double does not match declared output type string for node _recv_input_example_tensor_0 = _Recv[client_terminated=true, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=2016246895612781641, tensor_name="input_example_tensor:0", tensor_type=DT_STRING, _device="/job:localhost/replica:0/task:0/cpu:0"]()")
Here is the entire stack trace.
enter image description here
The iris model is defined in the following manner:
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3, model_dir=model_dir)
# Fit model.
classifier.fit(x=training_set.data,
y=training_set.target,
steps=2000)
Kindly guide a solution for this error.
I think the problem is that your signature_fn is going on the else branch and passing predictions as the output to the classification signature, which expects a string output and not a double output. Either use a regression signature function or add something to the graph to get the output in the form of a string.