diff options
| author | Lester Solbakken <lesters@oath.com> | 2020-06-12 12:16:56 +0200 |
|---|---|---|
| committer | Lester Solbakken <lesters@oath.com> | 2020-06-12 12:16:56 +0200 |
| commit | 0a886d74d4c9ffde41eef1f7e3c186b60b9f3726 (patch) | |
| tree | e142b94341563b28a2b4a0e26fe77458749d2ed9 /application | |
| parent | 8de8ff4f87295d812d4e660f0216953726200c92 (diff) | |
Import Tensorflow models vis ONNX conversion
Diffstat (limited to 'application')
10 files changed, 7 insertions, 14069 deletions
diff --git a/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx b/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx Binary files differnew file mode 100644 index 00000000000..0d4bffa5b57 --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt deleted file mode 100644 index 5528aa99401..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt +++ /dev/null @@ -1,8830 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - meta_info_def { - stripped_op_list { - op { - name: "Add" - input_arg { - name: "x" - type_attr: "T" - } - input_arg { - name: "y" - type_attr: "T" - } - output_arg { - name: "z" - type_attr: "T" - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -# Common imports -import numpy as np -import tensorflow as tf - -from tensorflow.examples.tutorials.mnist import input_data -from datetime import datetime - -now = datetime.utcnow().strftime("%Y%m%d%H%M%S") -root_logdir = "tf_logs" -logdir = "{}/run-{}/".format(root_logdir, now) - -mnist = input_data.read_data_sets("/tmp/data/") -X_train = mnist.train.images -X_test = mnist.test.images -y_train = mnist.train.labels.astype("int") -y_test = mnist.test.labels.astype("int") - -n_inputs = 28*28 # MNIST -n_hidden1 = 300 -n_hidden2 = 100 -n_hidden3 = 40 -n_outputs = 10 - -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -y = tf.placeholder(tf.int64, shape=(None), name="y") - - -def neuron_layer(X, n_neurons, name, activation=None): - with tf.name_scope(name): - n_inputs = int(X.get_shape()[1]) - stddev = 2 / np.sqrt(n_inputs) - init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) - W = tf.Variable(init, name="weights") - b = tf.Variable(tf.zeros([n_neurons]), name="bias") - Z = tf.matmul(X, W) + b - if activation is not None: - return activation(Z) - else: - return Z - - -def leaky_relu(z, name=None): - return tf.maximum(0.01 * z, z, name=name) - -def leaky_relu_with_small_constant(z, name=None): - return tf.maximum(tf.constant(0.01, shape=[1]) * z, z, name=name) - -with tf.name_scope("dnn"): - hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) - hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=leaky_relu_with_small_constant) - logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) - -with tf.name_scope("loss"): - xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - loss = tf.reduce_mean(xentropy, name="loss") - -with tf.name_scope("train"): - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - training_op = optimizer.minimize(loss) - -with tf.name_scope("eval"): - correct = tf.nn.in_top_k(logits, y, 1) - accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -accuracy_summary = tf.summary.scalar('Accuracy', accuracy) -file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) - -with tf.Session() as sess: - init.run() - for epoch in range(n_epochs): - for iteration in range(mnist.train.num_examples // batch_size): - X_batch, y_batch = mnist.train.next_batch(batch_size) - sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) - acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) - acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) - - # Save summary for tensorboard - summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - file_writer.add_summary(summary_str, epoch) - - export_path = "saved" - print('Exporting trained model to ', export_path) - builder = tf.saved_model.builder.SavedModelBuilder(export_path) - signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':logits}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -file_writer.close() diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py deleted file mode 100644 index 3f4f794d2ac..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""A very simple MNIST classifier. - -See extensive documentation at -https://www.tensorflow.org/get_started/mnist/beginners -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import sys - -from tensorflow.examples.tutorials.mnist import input_data - -import tensorflow as tf - -FLAGS = None - - -def main(_): - # Import data - mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) - - # Create the model - x = tf.placeholder(tf.float32, [None, 784]) - - with tf.name_scope("layer"): - W = tf.Variable(tf.zeros([784, 10])) - b = tf.Variable(tf.zeros([10])) - y = tf.matmul(x, W) + b - - - # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) - - # The raw formulation of cross-entropy, - # - # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), - # reduction_indices=[1])) - # - # can be numerically unstable. - # - # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw - # outputs of 'y', and then average across the batch. - cross_entropy = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) - train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) - - sess = tf.InteractiveSession() - tf.global_variables_initializer().run() - # Train - for _ in range(1000): - batch_xs, batch_ys = mnist.train.next_batch(100) - sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) - - # Test trained model - correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print(sess.run(accuracy, feed_dict={x: mnist.test.images, - y_: mnist.test.labels})) - - # Save the model - export_path = "saved" - print('Exporting trained model to ', export_path) - builder = tf.saved_model.builder.SavedModelBuilder(export_path) - signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt deleted file mode 100644 index 05b0e4e0f29..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt +++ /dev/null @@ -1,5039 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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key: "y" - value { - name: "layer/add:0" - dtype: DT_FLOAT - tensor_shape { - dim { - size: -1 - } - dim { - size: 10 - } - } - } - } - method_name: "tensorflow/serving/predict" - } - } -} diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 Binary files differdeleted file mode 100644 index 826b0280abf..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 +++ /dev/null diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index Binary files differdeleted file mode 100644 index d00fc5b06ed..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index +++ /dev/null diff --git a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java index 3d7eed1e729..369b2cbe42b 100644 --- a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java +++ b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java @@ -45,7 +45,7 @@ public class ContainerModelEvaluationTest { } private void assertLoadedModels(JDisc jdisc) { { - String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"tensorflow_mnist_softmax_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_softmax_saved\",\"tensorflow_mnist_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_saved\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}"; + String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"onnx_softmax_func\":\"http://localhost/model-evaluation/v1/onnx_softmax_func\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}"; assertResponse("http://localhost/model-evaluation/v1", expected, jdisc); } @@ -60,9 +60,8 @@ public class ContainerModelEvaluationTest { } { - // Note: The specific response value here has not been verified - String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\",\"d1\":\"0\"},\"value\":-0.5066885003407351},{\"address\":{\"d0\":\"0\",\"d1\":\"1\"},\"value\":0.3912837743150205},{\"address\":{\"d0\":\"0\",\"d1\":\"2\"},\"value\":-0.12401806321703948},{\"address\":{\"d0\":\"0\",\"d1\":\"3\"},\"value\":-0.7019029168606575},{\"address\":{\"d0\":\"0\",\"d1\":\"4\"},\"value\":0.13120114146441697},{\"address\":{\"d0\":\"0\",\"d1\":\"5\"},\"value\":0.6611923203384626},{\"address\":{\"d0\":\"0\",\"d1\":\"6\"},\"value\":-0.22365810810026446},{\"address\":{\"d0\":\"0\",\"d1\":\"7\"},\"value\":-0.0740018307465809},{\"address\":{\"d0\":\"0\",\"d1\":\"8\"},\"value\":0.056492490256153896},{\"address\":{\"d0\":\"0\",\"d1\":\"9\"},\"value\":-0.18422015072393733}]}"; - assertResponse("http://localhost/model-evaluation/v1/tensorflow_mnist_saved/serving_default.y/eval?input=" + inputTensor(), expected, jdisc); + String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\"},\"value\":0.3006095290184021},{\"address\":{\"d0\":\"1\"},\"value\":0.33222490549087524},{\"address\":{\"d0\":\"2\"},\"value\":0.36716532707214355}]}"; + assertResponse("http://localhost/model-evaluation/v1/onnx_softmax_func/default.output/eval?input=" + inputTensor(), expected, jdisc); } } @@ -79,9 +78,10 @@ public class ContainerModelEvaluationTest { } private String inputTensor() { - Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor(d0[],d1[784])")); - for (int i = 0; i < 784; i++) - b.cell(0.0, 0, i); + Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor<float>(d0[3])")); + b.cell(0.1, 0); + b.cell(0.2, 1); + b.cell(0.3, 2); return URLEncoder.encode(b.build().toString(), StandardCharsets.UTF_8); } |