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author | Lester Solbakken <lesters@users.noreply.github.com> | 2021-05-19 14:26:28 +0200 |
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committer | GitHub <noreply@github.com> | 2021-05-19 14:26:28 +0200 |
commit | 97080252fac0ba45b58f9d0efb56603da518428f (patch) | |
tree | 75255be3447ca5272f1bac08693d15afb944c6cc /model-integration/src/test/java/ai | |
parent | 390a26e1a42486fefedef5468c86a781d1d833d1 (diff) |
Revert "Add ONNX-RT evaluator to model-integration module"
Diffstat (limited to 'model-integration/src/test/java/ai')
-rw-r--r-- | model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java | 93 |
1 files changed, 0 insertions, 93 deletions
diff --git a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java b/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java deleted file mode 100644 index 4b42e18d75e..00000000000 --- a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java +++ /dev/null @@ -1,93 +0,0 @@ -// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -package ai.vespa.modelintegration.evaluator; - -import com.yahoo.tensor.Tensor; -import com.yahoo.tensor.TensorType; -import org.junit.Test; - -import java.util.HashMap; -import java.util.Map; - -import static org.junit.Assert.assertEquals; - -/** - * @author lesters - */ -public class OnnxEvaluatorTest { - - @Test - public void testSimpleMoodel() { - OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/simple/simple.onnx"); - - // Input types - Map<String, TensorType> inputTypes = evaluator.getInputInfo(); - assertEquals(inputTypes.get("query_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[4])")); - assertEquals(inputTypes.get("attribute_tensor"), TensorType.fromSpec("tensor<float>(d0[4],d1[1])")); - assertEquals(inputTypes.get("bias_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])")); - - // Output types - Map<String, TensorType> outputTypes = evaluator.getOutputInfo(); - assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])")); - - // Evaluation - Map<String, Tensor> inputs = new HashMap<>(); - inputs.put("query_tensor", Tensor.from("tensor(d0[1],d1[4]):[0.1, 0.2, 0.3, 0.4]")); - inputs.put("attribute_tensor", Tensor.from("tensor(d0[4],d1[1]):[0.1, 0.2, 0.3, 0.4]")); - inputs.put("bias_tensor", Tensor.from("tensor(d0[1],d1[1]):[1.0]")); - - assertEquals(evaluator.evaluate(inputs).get("output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]")); - assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]")); - } - - @Test - public void testBatchDimension() { - OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/pytorch/one_layer.onnx"); - - // Input types - Map<String, TensorType> inputTypes = evaluator.getInputInfo(); - assertEquals(inputTypes.get("input"), TensorType.fromSpec("tensor<float>(d0[],d1[3])")); - - // Output types - Map<String, TensorType> outputTypes = evaluator.getOutputInfo(); - assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[],d1[1])")); - - // Evaluation - Map<String, Tensor> inputs = new HashMap<>(); - inputs.put("input", Tensor.from("tensor<float>(d0[2],d1[3]):[[0.1, 0.2, 0.3],[0.4,0.5,0.6]]")); - assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor<float>(d0[2],d1[1]):[0.6393113,0.67574286]")); - } - - @Test - public void testMatMul() { - String expected = "tensor<float>(d0[2],d1[4]):[38,44,50,56,83,98,113,128]"; - String input1 = "tensor<float>(d0[2],d1[3]):[1,2,3,4,5,6]"; - String input2 = "tensor<float>(d0[3],d1[4]):[1,2,3,4,5,6,7,8,9,10,11,12]"; - assertEvaluate("simple/matmul.onnx", expected, input1, input2); - } - - @Test - public void testTypes() { - assertEvaluate("add_double.onnx", "tensor(d0[1]):[3]", "tensor(d0[1]):[1]", "tensor(d0[1]):[2]"); - assertEvaluate("add_float.onnx", "tensor<float>(d0[1]):[3]", "tensor<float>(d0[1]):[1]", "tensor<float>(d0[1]):[2]"); - assertEvaluate("add_int64.onnx", "tensor<double>(d0[1]):[3]", "tensor<double>(d0[1]):[1]", "tensor<double>(d0[1]):[2]"); - assertEvaluate("cast_int8_float.onnx", "tensor<float>(d0[1]):[-128]", "tensor<int8>(d0[1]):[128]"); - assertEvaluate("cast_float_int8.onnx", "tensor<int8>(d0[1]):[-1]", "tensor<float>(d0[1]):[255]"); - - // ONNX Runtime 1.7.0 does not support much of bfloat16 yet - // assertEvaluate("cast_bfloat16_float.onnx", "tensor<float>(d0[1]):[1]", "tensor<bfloat16>(d0[1]):[1]"); - } - - private void assertEvaluate(String model, String output, String... input) { - OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/" + model); - Map<String, Tensor> inputs = new HashMap<>(); - for (int i = 0; i < input.length; ++i) { - inputs.put("input" + (i+1), Tensor.from(input[i])); - } - Tensor expected = Tensor.from(output); - Tensor result = evaluator.evaluate(inputs, "output"); - assertEquals(expected, result); - assertEquals(expected.type().valueType(), result.type().valueType()); - } - -} |