Test mnist model

7 files changed, 8125 insertions, 1 deletions

diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/MlModelsImportingTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/MlModelsImportingTest.java
index f236bbd4467..6e55c0c9a53 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/MlModelsImportingTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/MlModelsImportingTest.java
@@ -67,7 +67,7 @@ public class MlModelsImportingTest {
                                   tfMnist);
             FunctionEvaluator evaluator = tfMnist.evaluatorOf("serving_default"); // TODO: Macro is offered as an alternative output currently, so need to specify argument
             assertEquals("constant(mnist_saved_dnn_hidden1_bias_read), constant(mnist_saved_dnn_hidden1_weights_read), constant(mnist_saved_dnn_hidden2_bias_read), constant(mnist_saved_dnn_hidden2_weights_read), constant(mnist_saved_dnn_outputs_bias_read), constant(mnist_saved_dnn_outputs_weights_read), input, rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add)", evaluator.context().names().stream().sorted().collect(Collectors.joining(", ")));
-            assertEquals(-0.714629131972222, evaluator.evaluate().sum().asDouble(), delta); // TODO: Verify in TF native
+            assertEquals(-0.714629131972222, evaluator.evaluate().sum().asDouble(), delta);
         }
     }
 
diff --git a/searchlib/src/test/files/integration/tensorflow/mnist/saved/saved_model.pbtxt b/searchlib/src/test/files/integration/tensorflow/mnist/saved/saved_model.pbtxt
new file mode 100644
index 00000000000..eb926836576
--- /dev/null
+++ b/searchlib/src/test/files/integration/tensorflow/mnist/saved/saved_model.pbtxt
@@ -0,0 +1,7982 @@
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+      }
+    }
+    node {
+      name: "save/RestoreV2_1/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+    }
+    node {
+      name: "save/RestoreV2_1/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
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+    node {
+      name: "save/RestoreV2_1"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_1/tensor_names"
+      input: "save/RestoreV2_1/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      attr {
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+    node {
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+      op: "Assign"
+      input: "dnn/hidden1/weights"
+      input: "save/RestoreV2_1"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
+        key: "_class"
+        value {
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+      }
+      attr {
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+        value {
+          list {
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+              dim {
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+          }
+        }
+      }
+      attr {
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+        value {
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+        }
+      }
+      attr {
+        key: "validate_shape"
+        value {
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_2/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+    }
+    node {
+      name: "save/RestoreV2_2/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      }
+    }
+    node {
+      name: "save/RestoreV2_2"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_2/tensor_names"
+      input: "save/RestoreV2_2/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
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+            }
+          }
+        }
+      }
+      attr {
+        key: "dtypes"
+        value {
+          list {
+            type: DT_FLOAT
+          }
+        }
+      }
+    }
+    node {
+      name: "save/Assign_2"
+      op: "Assign"
+      input: "dnn/hidden2/bias"
+      input: "save/RestoreV2_2"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
+        key: "_class"
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+        }
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+      attr {
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+      attr {
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+        value {
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+    }
+    node {
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+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
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+    node {
+      name: "save/RestoreV2_3/shape_and_slices"
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+      attr {
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+        value {
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+            tensor_shape {
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+      }
+    }
+    node {
+      name: "save/RestoreV2_3"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_3/tensor_names"
+      input: "save/RestoreV2_3/shape_and_slices"
+      attr {
+        key: "_output_shapes"
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+      attr {
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+    }
+    node {
+      name: "save/Assign_3"
+      op: "Assign"
+      input: "dnn/hidden2/weights"
+      input: "save/RestoreV2_3"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
+        key: "_class"
+        value {
+          list {
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+          }
+        }
+      }
+      attr {
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+        value {
+          list {
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+      }
+      attr {
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+        }
+      }
+      attr {
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+        value {
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+      }
+    }
+    node {
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+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      attr {
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+      attr {
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+            string_val: "dnn/outputs/bias"
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_4/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      attr {
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+    }
+    node {
+      name: "save/RestoreV2_4"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_4/tensor_names"
+      input: "save/RestoreV2_4/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      attr {
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+        value {
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+    node {
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+      op: "Assign"
+      input: "dnn/outputs/bias"
+      input: "save/RestoreV2_4"
+      attr {
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+      }
+      attr {
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+        value {
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+      attr {
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+        value {
+          list {
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+              dim {
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+              }
+            }
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+        }
+      }
+      attr {
+        key: "use_locking"
+        value {
+          b: true
+        }
+      }
+      attr {
+        key: "validate_shape"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+              dim {
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
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+            }
+          }
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+      }
+      attr {
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+        }
+      }
+      attr {
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+        value {
+          tensor {
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+            tensor_shape {
+              dim {
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+            string_val: ""
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_5/tensor_names"
+      input: "save/RestoreV2_5/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+          }
+        }
+      }
+      attr {
+        key: "dtypes"
+        value {
+          list {
+            type: DT_FLOAT
+          }
+        }
+      }
+    }
+    node {
+      name: "save/Assign_5"
+      op: "Assign"
+      input: "dnn/outputs/weights"
+      input: "save/RestoreV2_5"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
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+        value {
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+              dim {
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+          }
+        }
+      }
+      attr {
+        key: "use_locking"
+        value {
+          b: true
+        }
+      }
+      attr {
+        key: "validate_shape"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/restore_shard"
+      op: "NoOp"
+      input: "^save/Assign"
+      input: "^save/Assign_1"
+      input: "^save/Assign_2"
+      input: "^save/Assign_3"
+      input: "^save/Assign_4"
+      input: "^save/Assign_5"
+    }
+    node {
+      name: "save/restore_all"
+      op: "NoOp"
+      input: "^save/restore_shard"
+    }
+    versions {
+      producer: 24
+    }
+  }
+  saver_def {
+    filename_tensor_name: "save/Const:0"
+    save_tensor_name: "save/Identity:0"
+    restore_op_name: "save/restore_all"
+    max_to_keep: 5
+    sharded: true
+    keep_checkpoint_every_n_hours: 10000.0
+    version: V2
+  }
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+    value {
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+          tensor_shape {
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+      outputs {
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+        value {
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+          dtype: DT_FLOAT
+          tensor_shape {
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+}
diff --git a/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 b/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
new file mode 100644
index 00000000000..a7ca01888c7
--- /dev/null
+++ b/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
diff --git a/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.index b/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.index
new file mode 100644
index 00000000000..7989c109a3a
--- /dev/null
+++ b/searchlib/src/test/files/integration/tensorflow/mnist/saved/variables/variables.index
diff --git a/searchlib/src/test/files/integration/tensorflow/mnist/simple_mnist.py b/searchlib/src/test/files/integration/tensorflow/mnist/simple_mnist.py
new file mode 100644
index 00000000000..26529f67919
--- /dev/null
+++ b/searchlib/src/test/files/integration/tensorflow/mnist/simple_mnist.py
@@ -0,0 +1,97 @@
+
+# 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)
+
+
+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=tf.nn.selu)
+    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()
\ No newline at end of file
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java
new file mode 100644
index 00000000000..add66eece1a
--- /dev/null
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java
@@ -0,0 +1,30 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.ml;
+
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import org.junit.Test;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+
+/**
+ * @author bratseth
+ */
+public class MnistImportTestCase {
+
+    @Test
+    public void testMnistImport() {
+        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/files/integration/tensorflow/mnist/saved");
+        ImportedModel.Signature signature = model.get().signature("serving_default");
+
+        assertEquals("Has skipped outputs",
+                     0, model.get().signature("serving_default").skippedOutputs().size());
+
+        RankingExpression output = signature.outputExpression("y");
+        assertNotNull(output);
+        assertEquals("dnn/outputs/add", output.getName());
+        model.assertEqualResultSum("input", output.getName(), 0.00001);
+    }
+
+
+}
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java
index 273eafad0d9..4de3aa5d635 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java
@@ -41,6 +41,21 @@ public class TestableTensorFlowModel {
 
     public ImportedModel get() { return model; }
 
+    /** Compare that summing the tensors produce the same result to within some tolerance delta */
+    public void assertEqualResultSum(String inputName, String operationName, double delta) {
+        Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
+        Context context = contextFrom(model);
+        Tensor placeholder = placeholderArgument();
+        context.put(inputName, new TensorValue(placeholder));
+
+        model.macros().forEach((k,v) -> evaluateMacro(context, model, k));
+
+        Tensor vespaResult = model.expressions().get(operationName).evaluate(context).asTensor();
+        assertEquals("Operation '" + operationName + "' produces equal results",
+                     tfResult.sum().asDouble(), vespaResult.sum().asDouble(), delta);
+    }
+
+    /** Compare tensors 100% exactly */
     public void assertEqualResult(String inputName, String operationName) {
         Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
         Context context = contextFrom(model);