diff options
author | Henning Baldersheim <balder@yahoo-inc.com> | 2022-12-04 22:42:13 +0100 |
---|---|---|
committer | Henning Baldersheim <balder@yahoo-inc.com> | 2022-12-04 22:42:13 +0100 |
commit | fd54b4e4b136b05b67b471dca3ca6ce3dd116710 (patch) | |
tree | 88ab77cc41620eb2b8639b10b7edd1e57b901f12 /vespajlib/src/main/java/com/yahoo/tensor/serialization | |
parent | fae49834f53500587921196e86ee4af4c670d8c9 (diff) |
Revert collect(Collectors.toList())
Diffstat (limited to 'vespajlib/src/main/java/com/yahoo/tensor/serialization')
-rw-r--r-- | vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java | 2 | ||||
-rw-r--r-- | vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java | 8 |
2 files changed, 5 insertions, 5 deletions
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java index a7afc1efc6d..b683519988c 100644 --- a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java +++ b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java @@ -124,7 +124,7 @@ public class JsonFormat { private static void encodeBlocks(MixedTensor tensor, Cursor cursor) { var mappedDimensions = tensor.type().dimensions().stream().filter(d -> d.isMapped()) - .map(d -> TensorType.Dimension.mapped(d.name())).toList(); + .map(d -> TensorType.Dimension.mapped(d.name())).collect(Collectors.toList()); if (mappedDimensions.size() < 1) { throw new IllegalArgumentException("Should be ensured by caller"); } diff --git a/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java b/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java index c18e9f179d6..2a55ac658b1 100644 --- a/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java +++ b/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java @@ -43,7 +43,7 @@ class MixedBinaryFormat implements BinaryFormat { } private void encodeSparseDimensions(GrowableByteBuffer buffer, MixedTensor tensor) { - List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).toList(); + List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList()); buffer.putInt1_4Bytes(sparseDimensions.size()); for (TensorType.Dimension dimension : sparseDimensions) { buffer.putUtf8String(dimension.name()); @@ -51,7 +51,7 @@ class MixedBinaryFormat implements BinaryFormat { } private void encodeDenseDimensions(GrowableByteBuffer buffer, MixedTensor tensor) { - List<TensorType.Dimension> denseDimensions = tensor.type().dimensions().stream().filter(d -> d.isIndexed()).toList(); + List<TensorType.Dimension> denseDimensions = tensor.type().dimensions().stream().filter(d -> d.isIndexed()).collect(Collectors.toList()); buffer.putInt1_4Bytes(denseDimensions.size()); for (TensorType.Dimension dimension : denseDimensions) { buffer.putUtf8String(dimension.name()); @@ -71,7 +71,7 @@ class MixedBinaryFormat implements BinaryFormat { } private void encodeCells(GrowableByteBuffer buffer, MixedTensor tensor, Consumer<Double> consumer) { - List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).toList(); + List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList()); long denseSubspaceSize = tensor.denseSubspaceSize(); if (sparseDimensions.size() > 0) { buffer.putInt1_4Bytes((int)(tensor.size() / denseSubspaceSize)); // XXX: Size truncation @@ -137,7 +137,7 @@ class MixedBinaryFormat implements BinaryFormat { } private void decodeCells(GrowableByteBuffer buffer, MixedTensor.BoundBuilder builder, TensorType type, Supplier<Double> supplier) { - List<TensorType.Dimension> sparseDimensions = type.dimensions().stream().filter(d -> !d.isIndexed()).toList(); + List<TensorType.Dimension> sparseDimensions = type.dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList()); TensorType sparseType = MixedTensor.createPartialType(type.valueType(), sparseDimensions); long denseSubspaceSize = builder.denseSubspaceSize(); |