org.nd4j.shade.jackson.core.JsonParser Java Examples
The following examples show how to use
org.nd4j.shade.jackson.core.JsonParser.
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Example #1
Source File: IntegerDistributionDeserializer.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Override public IntegerDistribution deserialize(JsonParser p, DeserializationContext ctxt) throws IOException { JsonNode node = p.getCodec().readTree(p); String simpleName = node.get("distribution").asText(); switch (simpleName) { case "BinomialDistribution": return new BinomialDistribution(node.get("trials").asInt(), node.get("p").asDouble()); case "GeometricDistribution": return new GeometricDistribution(node.get("p").asDouble()); case "HypergeometricDistribution": return new HypergeometricDistribution(node.get("populationSize").asInt(), node.get("numberOfSuccesses").asInt(), node.get("sampleSize").asInt()); case "PascalDistribution": return new PascalDistribution(node.get("r").asInt(), node.get("p").asDouble()); case "PoissonDistribution": return new PoissonDistribution(node.get("p").asDouble()); case "UniformIntegerDistribution": return new UniformIntegerDistribution(node.get("lower").asInt(), node.get("upper").asInt()); case "ZipfDistribution": return new ZipfDistribution(node.get("numElements").asInt(), node.get("exponent").asDouble()); default: throw new RuntimeException("Unknown or not supported distribution: " + simpleName); } }
Example #2
Source File: DataFormatDeserializer.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Override public DataFormat deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jp.getCodec().readTree(jp); String text = node.textValue(); switch (text){ case "NCHW": return CNN2DFormat.NCHW; case "NHWC": return CNN2DFormat.NHWC; case "NCW": return RNNFormat.NCW; case "NWC": return RNNFormat.NWC; default: return null; } }
Example #3
Source File: LegacyIntArrayDeserializer.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Override public int[] deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode n = jp.getCodec().readTree(jp); if(n.isArray()){ ArrayNode an = (ArrayNode)n; int size = an.size(); int[] out = new int[size]; for( int i=0; i<size; i++ ){ out[i] = an.get(i).asInt(); } return out; } else if(n.isNumber()){ int v = n.asInt(); return new int[]{v,v}; } else { throw new IllegalStateException("Could not deserialize value: " + n); } }
Example #4
Source File: RowVectorDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public INDArray deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); if (node == null) return null; int size = node.size(); double[] d = new double[size]; for (int i = 0; i < size; i++) { d[i] = node.get(i).asDouble(); } return Nd4j.create(d); }
Example #5
Source File: ConfusionMatrixDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public ConfusionMatrix<Integer> deserialize(JsonParser jp, DeserializationContext ctxt) throws IOException, JsonProcessingException { JsonNode n = jp.getCodec().readTree(jp); //Get class names/labels ArrayNode classesNode = (ArrayNode) n.get("classes"); List<Integer> classes = new ArrayList<>(); for (JsonNode cn : classesNode) { classes.add(cn.asInt()); } ConfusionMatrix<Integer> cm = new ConfusionMatrix<>(classes); ObjectNode matrix = (ObjectNode) n.get("matrix"); Iterator<Map.Entry<String, JsonNode>> matrixIter = matrix.fields(); while (matrixIter.hasNext()) { Map.Entry<String, JsonNode> e = matrixIter.next(); int actualClass = Integer.parseInt(e.getKey()); ArrayNode an = (ArrayNode) e.getValue(); ArrayNode innerMultiSetKey = (ArrayNode) an.get(0); ArrayNode innerMultiSetCount = (ArrayNode) an.get(1); Iterator<JsonNode> iterKey = innerMultiSetKey.iterator(); Iterator<JsonNode> iterCnt = innerMultiSetCount.iterator(); while (iterKey.hasNext()) { int predictedClass = iterKey.next().asInt(); int count = iterCnt.next().asInt(); cm.add(actualClass, predictedClass, count); } } return cm; }
Example #6
Source File: BoundingBoxesDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jp.getCodec().readTree(jp); if(node.has("dataBuffer")){ //Must be legacy format serialization JsonNode arr = node.get("dataBuffer"); int rank = node.get("rankField").asInt(); int numElements = node.get("numElements").asInt(); int offset = node.get("offsetField").asInt(); JsonNode shape = node.get("shapeField"); JsonNode stride = node.get("strideField"); int[] shapeArr = new int[rank]; int[] strideArr = new int[rank]; DataBuffer buff = Nd4j.createBuffer(numElements); for (int i = 0; i < numElements; i++) { buff.put(i, arr.get(i).asDouble()); } String ordering = node.get("orderingField").asText(); for (int i = 0; i < rank; i++) { shapeArr[i] = shape.get(i).asInt(); strideArr[i] = stride.get(i).asInt(); } return Nd4j.create(buff, shapeArr, strideArr, offset, ordering.charAt(0)); } //Standard/new format return new NDArrayTextDeSerializer().deserialize(node); }
Example #7
Source File: DateTimeFieldTypeDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public DateTimeFieldType deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); String value = node.get("fieldType").textValue(); return map.get(value); }
Example #8
Source File: TDigestDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public TDigest deserialize(JsonParser jp, DeserializationContext d) throws IOException, JsonProcessingException { JsonNode node = (JsonNode)jp.getCodec().readTree(jp); String field = node.get("digest").asText(); Base64 b = new Base64(); byte[] bytes = b.decode(field); try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes))){ return (TDigest) ois.readObject(); } catch (Exception e){ throw new RuntimeException("Error deserializing TDigest object from JSON", e); } }
Example #9
Source File: NDArrayDeSerializer.java From nd4j with Apache License 2.0 | 5 votes |
@Override public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException { JsonNode node = jp.getCodec().readTree(jp); String field = node.get("array").asText(); INDArray ret = Nd4jBase64.fromBase64(field.toString()); return ret; }
Example #10
Source File: VectorDeSerializer.java From nd4j with Apache License 2.0 | 5 votes |
@Override public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException { JsonNode node = jp.getCodec().readTree(jp); JsonNode arr = node.get("dataBuffer"); int rank = node.get("rankField").asInt(); int numElements = node.get("numElements").asInt(); int offset = node.get("offsetField").asInt(); JsonNode shape = node.get("shapeField"); JsonNode stride = node.get("strideField"); String type = node.get("typeField").asText(); int[] realShape = new int[rank]; int[] realStride = new int[rank]; DataBuffer buff = Nd4j.createBuffer(numElements); for (int i = 0; i < numElements; i++) { buff.put(i, arr.get(i).asDouble()); } String ordering = node.get("orderingField").asText(); for (int i = 0; i < rank; i++) { realShape[i] = shape.get(i).asInt(); realStride[i] = stride.get(i).asInt(); } INDArray ret = type.equals("real") ? Nd4j.create(buff, realShape, realStride, offset, ordering.charAt(0)) : Nd4j.createComplex(buff, realShape, realStride, offset, ordering.charAt(0)); return ret; }
Example #11
Source File: RowVectorDeserializer.java From nd4j with Apache License 2.0 | 5 votes |
@Override public INDArray deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); if (node == null) return null; int size = node.size(); double[] d = new double[size]; for (int i = 0; i < size; i++) { d[i] = node.get(i).asDouble(); } return Nd4j.create(d); }
Example #12
Source File: DateTimeFieldTypeDeserializer.java From DataVec with Apache License 2.0 | 5 votes |
@Override public DateTimeFieldType deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); String value = node.get("fieldType").textValue(); return map.get(value); }
Example #13
Source File: TDigestDeserializer.java From DataVec with Apache License 2.0 | 5 votes |
@Override public TDigest deserialize(JsonParser jp, DeserializationContext d) throws IOException, JsonProcessingException { JsonNode node = (JsonNode)jp.getCodec().readTree(jp); String field = node.get("digest").asText(); Base64 b = new Base64(); byte[] bytes = b.decode(field); try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes))){ return (TDigest) ois.readObject(); } catch (Exception e){ throw new RuntimeException("Error deserializing TDigest object from JSON", e); } }
Example #14
Source File: PointDeserializer.java From konduit-serving with Apache License 2.0 | 5 votes |
@Override public Point deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException { JsonNode n = jp.getCodec().readTree(jp); String lbl = n.has("label") ? n.get("label").textValue() : null; Double prob = n.has("probability") ? n.get("probability").doubleValue() : null; ArrayNode cn = (ArrayNode)n.get("coords"); double[] pts = new double[cn.size()]; for( int i=0; i<pts.length; i++ ){ pts[i] = cn.get(i).doubleValue(); } return new NDPoint(pts, lbl, prob); }
Example #15
Source File: StorageLevelDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public StorageLevel deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); String value = node.textValue(); if (value == null || "null".equals(value)) { return null; } return StorageLevel.fromString(value); }
Example #16
Source File: BaseNetConfigDeserializer.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public abstract T deserialize(JsonParser jp, DeserializationContext ctxt) throws IOException, JsonProcessingException;
Example #17
Source File: NDArrayDeSerializer.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException { JsonNode node = jp.getCodec().readTree(jp); String field = node.get("array").asText(); return Nd4jBase64.fromBase64(field); }
Example #18
Source File: BoundingBoxDeserializer.java From konduit-serving with Apache License 2.0 | 4 votes |
@Override public BoundingBox deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException { JsonNode n = jp.getCodec().readTree(jp); return DataJsonDeserializer.deserializeBB(n); }
Example #19
Source File: RealDistributionDeserializer.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public RealDistribution deserialize(JsonParser p, DeserializationContext ctxt) throws IOException, JsonProcessingException { JsonNode node = p.getCodec().readTree(p); String simpleName = node.get("distribution").asText(); switch (simpleName) { case "BetaDistribution": return new BetaDistribution(node.get("alpha").asDouble(), node.get("beta").asDouble()); case "CauchyDistribution": return new CauchyDistribution(node.get("median").asDouble(), node.get("scale").asDouble()); case "ChiSquaredDistribution": return new ChiSquaredDistribution(node.get("dof").asDouble()); case "ExponentialDistribution": return new ExponentialDistribution(node.get("mean").asDouble()); case "FDistribution": return new FDistribution(node.get("numeratorDof").asDouble(), node.get("denominatorDof").asDouble()); case "GammaDistribution": return new GammaDistribution(node.get("shape").asDouble(), node.get("scale").asDouble()); case "LevyDistribution": return new LevyDistribution(node.get("mu").asDouble(), node.get("c").asDouble()); case "LogNormalDistribution": return new LogNormalDistribution(node.get("scale").asDouble(), node.get("shape").asDouble()); case "NormalDistribution": return new NormalDistribution(node.get("mean").asDouble(), node.get("stdev").asDouble()); case "ParetoDistribution": return new ParetoDistribution(node.get("scale").asDouble(), node.get("shape").asDouble()); case "TDistribution": return new TDistribution(node.get("dof").asDouble()); case "TriangularDistribution": return new TriangularDistribution(node.get("a").asDouble(), node.get("b").asDouble(), node.get("c").asDouble()); case "UniformRealDistribution": return new UniformRealDistribution(node.get("lower").asDouble(), node.get("upper").asDouble()); case "WeibullDistribution": return new WeibullDistribution(node.get("alpha").asDouble(), node.get("beta").asDouble()); case "LogUniformDistribution": return new LogUniformDistribution(node.get("min").asDouble(), node.get("max").asDouble()); default: throw new RuntimeException("Unknown or not supported distribution: " + simpleName); } }
Example #20
Source File: NDArrayTextDeSerializer.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException { JsonNode n = jp.getCodec().readTree(jp); return deserialize(n); }
Example #21
Source File: JsonDeserializerAtomicBoolean.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public AtomicBoolean deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); boolean value = node.asBoolean(); return new AtomicBoolean(value); }
Example #22
Source File: JsonDeserializerAtomicDouble.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public AtomicDouble deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); double value = node.asDouble(); return new AtomicDouble(value); }
Example #23
Source File: JsonDeserializerAtomicBoolean.java From nd4j with Apache License 2.0 | 4 votes |
@Override public AtomicBoolean deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); boolean value = node.asBoolean(); return new AtomicBoolean(value); }
Example #24
Source File: JsonDeserializerAtomicDouble.java From nd4j with Apache License 2.0 | 4 votes |
@Override public AtomicDouble deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException { JsonNode node = jsonParser.getCodec().readTree(jsonParser); double value = node.asDouble(); return new AtomicDouble(value); }
Example #25
Source File: DataJsonDeserializer.java From konduit-serving with Apache License 2.0 | 4 votes |
protected Pair<List<Object>, ValueType> deserializeList(JsonParser jp, JsonNode n){ ArrayNode an = (ArrayNode)n; int size = an.size(); //TODO PROBLEM: empty list type is ambiguous! Preconditions.checkState(size > 0, "Unable to deserialize empty lists (not yet implemented)"); JsonNode n3 = n.get(0); ValueType listType = nodeType(n3); List<Object> list = new ArrayList<>(); switch (listType){ case NDARRAY: for( int i=0; i<size; i++ ){ list.add(deserializeNDArray(n.get(i))); } break; case STRING: for( int i=0; i<size; i++ ){ list.add(n.get(i).textValue()); } break; case BYTES: for( int i=0; i<size; i++ ){ list.add(deserializeBytes(n.get(i))); } break; case IMAGE: for( int i=0; i<size; i++ ){ list.add(deserializeImage(n.get(i))); } break; case DOUBLE: for( int i=0; i<size; i++ ){ list.add(n.get(i).doubleValue()); } break; case INT64: for( int i=0; i<size; i++ ){ list.add(n.get(i).longValue()); } break; case BOOLEAN: for( int i=0; i<size; i++ ){ list.add(n.get(i).booleanValue()); } break; case DATA: for( int i=0; i<size; i++ ){ list.add(deserialize(jp, n.get(i))); } break; case LIST: for( int i=0; i<size; i++ ){ list.add(deserializeList(jp, n.get(i))); } break; case BOUNDING_BOX: for( int i=0; i<size; i++ ){ list.add(deserializeBB(n.get(i))); } break; case POINT: for( int i=0; i<size; i++ ){ list.add(deserializePoint(n.get(i))); } break; default: throw new IllegalStateException("Unable to deserialize list with values of type: " + listType); } return new Pair<>(list, listType); }
Example #26
Source File: DataJsonDeserializer.java From konduit-serving with Apache License 2.0 | 4 votes |
public Data deserialize(JsonParser jp, JsonNode n) { JData d = new JData(); Iterator<String> names = n.fieldNames(); while (names.hasNext()) { String s = names.next(); JsonNode n2 = n.get(s); if (Data.RESERVED_KEY_METADATA.equalsIgnoreCase(s)) { Data meta = deserialize(jp, n2); d.setMetaData(meta); } else { if (n2.isTextual()) { //String String str = n2.textValue(); d.put(s, str); } else if (n2.isDouble()) { //Double double dVal = n2.doubleValue(); d.put(s, dVal); } else if (n2.isInt() || n2.isLong()) { //Long long lVal = n2.longValue(); d.put(s, lVal); } else if (n2.isBoolean()) { //Boolean boolean b = n2.booleanValue(); d.put(s, b); } else if (n2.isArray()){ Pair<List<Object>, ValueType> p = deserializeList(jp, n2); d.putList(s, p.getFirst(), p.getSecond()); } else if (n2.isObject()) { //Could be: Bytes, image, NDArray, BoundingBox, Point or Data if (n2.has(Data.RESERVED_KEY_BYTES_BASE64) || n2.has(Data.RESERVED_KEY_BYTES_ARRAY)) { //byte[] stored in base64 or byte[] as JSON array byte[] bytes = deserializeBytes(n2); d.put(s, bytes); } else if (n2.has(Data.RESERVED_KEY_NDARRAY_TYPE)) { //NDArray d.put(s, deserializeNDArray(n2)); } else if (n2.has(Data.RESERVED_KEY_IMAGE_DATA)) { //Image d.put(s, deserializeImage(n2)); } else if(n2.has(Data.RESERVED_KEY_BB_CY) || n2.has(Data.RESERVED_KEY_BB_X1)){ d.put(s, deserializeBB(n2)); } else if(n2.has(Data.RESERVED_KEY_POINT_COORDS)){ d.put(s, deserializePoint(n2)); } else { //Must be data Data dInner = deserialize(jp, n2); d.put(s, dInner); } } else { throw new UnsupportedOperationException("Type not yet implemented"); } } } return d; }
Example #27
Source File: DataJsonDeserializer.java From konduit-serving with Apache License 2.0 | 4 votes |
@Override public Data deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException { JsonNode n = jp.getCodec().readTree(jp); return deserialize(jp, n); }
Example #28
Source File: PipelineDeserializer.java From konduit-serving with Apache License 2.0 | 4 votes |
@Override public Pipeline deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException { TreeNode tn = jp.readValueAsTree(); TreeNode n = tn.get("steps"); String id = null; if(tn.get("id") != null){ id = ((TextNode)tn.get("id")).asText(); } Trigger asyncTrigger = null; //If present: it's an async pipeline if(tn.get(Data.RESERVED_KEY_ASYNC_TRIGGER) != null){ TreeNode triggerNode = tn.get(Data.RESERVED_KEY_ASYNC_TRIGGER); asyncTrigger = jp.getCodec().treeToValue(triggerNode, Trigger.class); } Pipeline p; if(n.isArray()){ PipelineStep[] steps = jp.getCodec().treeToValue(n, PipelineStep[].class); p = new SequencePipeline(Arrays.asList(steps), id); } else if(n.isObject()){ Map<String, GraphStep> map = new LinkedHashMap<>(); Iterator<String> f = n.fieldNames(); while(f.hasNext()) { String s = f.next(); TreeNode pn = n.get(s); GraphStep step = jp.getCodec().treeToValue(pn, GraphStep.class); step.name(s); map.put(s, step); } String outputStep = ((TextNode)tn.get("outputStep")).asText(); p = new GraphPipeline(map, outputStep, id); } else { throw new JsonParseException(jp, "Unable to deserialize Pipeline: Invalid JSON/YAML? Pipeline is neither a SequencePipeline or a GraphPipeline"); } if(asyncTrigger != null ){ return new AsyncPipeline(p, asyncTrigger); } return p; }