Java Code Examples for org.nd4j.autodiff.samediff.SameDiff#outputAll()
The following examples show how to use
org.nd4j.autodiff.samediff.SameDiff#outputAll() .
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Example 1
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testStridedSlice2dBasic() {
INDArray inArr = Nd4j.linspace(1, 12, 12).reshape('c', 3, 4);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable slice_full = sd.stridedSlice(in,new long[]{0, 0},new long[]{3, 4},new long[]{1, 1});
SDVariable subPart = sd.stridedSlice(in,new long[]{1, 2},new long[]{3, 4},new long[]{1, 1});
// SDVariable subPart2 = sd.stridedSlice(in,new long[]{0, 0},new long[]{4, 5},new long[]{2, 2});
sd.outputAll(null);
assertEquals(inArr, slice_full.getArr());
assertEquals(inArr.get(interval(1, 3), interval(2, 4)), subPart.getArr());
// assertEquals(inArr.get(interval(0, 2, 4), interval(0, 2, 5)), subPart2.getArr());
}
Example 2
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testStridedSliceEllipsisMask() {
INDArray inArr = Nd4j.linspace(1, 60, 60).reshape('c', 3, 4, 5);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
//[1:3,...] -> [1:3,:,:]
SDVariable slice = sd.stridedSlice(in,new long[]{1},new long[]{3},new long[]{1}, 0, 0, 1 << 1, 0, 0);
//[1:3,...,1:4] -> [1:3,:,1:4]
SDVariable slice2 = sd.stridedSlice(in,new long[]{1, 1},new long[]{3, 4},new long[]{1, 1}, 0, 0, 1 << 1, 0, 0);
sd.outputAll(Collections.emptyMap());
assertEquals(inArr.get(interval(1, 3), all(), all()), slice.getArr());
assertEquals(inArr.get(interval(1, 3), all(), all()), slice2.getArr());
}
Example 3
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testStridedSliceShrinkAxisMask() {
INDArray inArr = Nd4j.linspace(1, 60, 60).reshape('c', 3, 4, 5);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable slice = sd.stridedSlice(in,new long[]{0, 0, 0},new long[]{-999, 4, 5},new long[]{1, 1, 1}, 0, 0, 0, 0, 1);
SDVariable slice2 = sd.stridedSlice(in,new long[]{2, 0, 0},new long[]{-999, 4, 5},new long[]{1, 1, 1}, 0, 0, 0, 0, 1);
SDVariable slice3 = sd.stridedSlice(in,new long[]{1, 2, 1},new long[]{-999, -999, 5},new long[]{1, 1, 1}, 0, 0, 0, 0, 1 | 1 << 1);
sd.outputAll(null);
assertEquals(inArr.get(point(0), all(), all()), slice.getArr());
assertEquals(inArr.get(point(2), all(), all()), slice2.getArr());
assertEquals(inArr.get(point(1), point(2), interval(1, 5)).reshape(4), slice3.getArr());
}
Example 4
| Source File: SameDiffInferenceExecutionerTests.java From konduit-serving with Apache License 2.0 | 5 votes |
|
@Test(timeout = 60000)
public void testSameDiff() throws Exception {
SameDiffInferenceExecutioner sameDiffInferenceExecutioner = new SameDiffInferenceExecutioner();
SameDiff sameDiff = SameDiff.create();
SDVariable input1 = sameDiff.placeHolder("input1", DataType.FLOAT,2, 2);
SDVariable input2 = sameDiff.placeHolder("input2", DataType.FLOAT,2, 2);
SDVariable result = input1.add("output", input2);
INDArray input1Arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
INDArray input2Arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
sameDiff.associateArrayWithVariable(input1Arr, input1.name());
sameDiff.associateArrayWithVariable(input2Arr, input2.name());
Map<String, INDArray> indArrays = new LinkedHashMap<>();
indArrays.put(input1.name(), input1Arr);
indArrays.put(input2.name(), input2Arr);
Map<String, INDArray> outputs = sameDiff.outputAll(indArrays);
assertEquals(3, outputs.size());
ParallelInferenceConfig parallelInferenceConfig = ParallelInferenceConfig.defaultConfig();
File newFile = temporary.newFile();
sameDiff.asFlatFile(newFile);
SameDiffModelLoader sameDiffModelLoader = new SameDiffModelLoader(newFile, Arrays.asList("input1", "input2"), Arrays.asList("output"));
sameDiffInferenceExecutioner.initialize(sameDiffModelLoader, parallelInferenceConfig);
INDArray[] execute = sameDiffInferenceExecutioner.execute(new INDArray[]{input1Arr, input2Arr});
assertEquals(outputs.values().iterator().next(), execute[0]);
}
Example 5
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testPermuteSimple() {
SameDiff sameDiff = SameDiff.create();
INDArray arr = Transforms.sigmoid(Nd4j.linspace(1, 6, 6).reshape(2, 3));
SDVariable x = sameDiff.var("x", arr);
SDVariable result = sameDiff.permute(x, 1, 0);
Map<String,INDArray> m = sameDiff.outputAll(null);
assertArrayEquals(new long[]{3, 2}, m.get(result.name()).shape());
}
Example 6
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSlice2d() {
INDArray inArr = Nd4j.linspace(1, 12, 12).reshape('c', 3, 4);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable slice_full = sd.slice(in, new int[]{0, 0}, new int[]{3, 4});
SDVariable subPart = sd.slice(in, new int[]{1, 2}, new int[]{2, 2});
Map<String,INDArray> m = sd.outputAll(Collections.emptyMap());
assertEquals(inArr, m.get(slice_full.name()));
assertEquals(inArr.get(interval(1, 3), interval(2, 4)), m.get(subPart.name()));
}
Example 7
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSlice3d() {
INDArray inArr = Nd4j.linspace(1, 60, 60).reshape('c', 3, 4, 5);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable slice_full = sd.slice(in, new int[]{0, 0, 0}, new int[]{3, 4, 5});
SDVariable subPart = sd.slice(in, new int[]{1, 2, 3}, new int[]{2, 2, 1});
Map<String,INDArray> m = sd.outputAll(null);
assertEquals(inArr, m.get(slice_full.name()));
assertEquals(inArr.get(interval(1, 3), interval(2, 4), interval(3, 4)), m.get(subPart.name()));
}
Example 8
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testStridedSliceBeginEndMask() {
INDArray inArr = Nd4j.linspace(1, 12, 12).reshape('c', 3, 4);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable slice1 = sd.stridedSlice(in,new long[]{-999, 0},new long[]{2, 4},new long[]{1, 1}, 1 << 1, 0, 0, 0, 0);
SDVariable slice2 = sd.stridedSlice(in,new long[]{1, 0},new long[]{-999, 4},new long[]{1, 1}, 0, 1, 0, 0, 0);
sd.outputAll(null);
assertEquals(inArr.get(NDArrayIndex.interval(0, 2), NDArrayIndex.all()), slice1.getArr());
assertEquals(inArr.get(NDArrayIndex.interval(1, 3), NDArrayIndex.all()), slice2.getArr());
}
Example 9
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testRank0EdgeCase() {
SameDiff sd = SameDiff.create();
SDVariable v1 = sd.sum(sd.var(Nd4j.create(new double[]{4, 4})));
double d0 = v1.eval().getDouble(0);
assertEquals(8, d0, 0);
SDVariable v2 = sd.sum(sd.var(Nd4j.create(new double[]{4, 4}))).div(2.0);
Map<String, INDArray> m = sd.outputAll(Collections.emptyMap());
double d1 = m.get(v2.name()).getDouble(0);
assertEquals(4, d1, 0);
}
Example 10
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMulGradient() {
INDArray arr1 = Nd4j.linspace(1, 4, 4, DataType.DOUBLE).reshape(2, 2);
INDArray arr2 = Nd4j.linspace(1, 4, 4, DataType.DOUBLE).reshape(2, 2);
INDArray gradAssertion = Nd4j.ones(arr1.shape());
INDArray scalar = Nd4j.scalar(1.0);
INDArray aGradAssertion = Nd4j.create(new double[][]{
{1, 4},
{9, 16}
});
INDArray cGradAssertion = Nd4j.create(new double[][]{
{1, 2},
{3, 4}
});
INDArray wGradAssertion = Nd4j.create(new double[][]{
{2, 8},
{18, 32}
});
INDArray dGradAssertion = Nd4j.ones(2, 2);
SameDiff sameDiff = SameDiff.create();
SDVariable sdVariable = sameDiff.var("a", arr1);
SDVariable sdVariable1 = sameDiff.var("w", arr2);
SDVariable varMulPre = sdVariable.mul("c", sdVariable1);
SDVariable varMul = varMulPre.mul("d", sdVariable1);
SDVariable sum = sameDiff.sum("ret", varMul, Integer.MAX_VALUE);
Map<String,INDArray> m = sameDiff.outputAll(null);
Map<String,INDArray> gm = sameDiff.calculateGradients(null, m.keySet());
SDVariable finalResult = sameDiff.grad(sum.name());
SDVariable cGrad = sameDiff.grad(varMulPre.name());
SDVariable mulGradResult = sameDiff.grad(varMul.name());
SDVariable aGrad = sameDiff.grad(sdVariable.name());
SDVariable wGrad = sameDiff.grad(sdVariable1.name());
SDVariable dGrad = sameDiff.grad(varMul.name());
INDArray scalarGradTest = gm.get(sum.name());
assertEquals(scalar, scalarGradTest);
INDArray gradTest = mulGradResult.getArr();
assertEquals(gradAssertion, gradTest);
INDArray aGradTest = aGrad.getArr();
assertEquals(aGradAssertion, aGradTest);
INDArray cGradTest = cGrad.getArr();
assertEquals(cGradAssertion, cGradTest);
INDArray wGradTest = wGrad.getArr();
assertEquals(wGradAssertion, wGradTest);
INDArray dGradTest = dGrad.getArr();
assertEquals(dGradAssertion, dGradTest);
}
Example 11
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testExpandDimsGradient() {
val origShape = new long[]{3, 4};
List<String> failed = new ArrayList<>();
boolean first = true;
for (int i = 0; i < 3; i++) {
long[] expExpandShape;
switch (i) {
case 0:
expExpandShape = new long[]{1, 3, 4};
break;
case 1:
expExpandShape = new long[]{3, 1, 4};
break;
case 2:
expExpandShape = new long[]{3, 4, 1};
break;
default:
throw new RuntimeException();
}
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAllTestMatricesWithShape(origShape[0], origShape[1], 12345, DataType.DOUBLE)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable expand = sd.expandDims(in, i);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", expand, true);
Map<String,INDArray> m = sd.outputAll(null);
INDArray expOut = in.getArr().std(true);
assertArrayEquals(expExpandShape, m.get(expand.name()).shape());
INDArray expExpand = inArr.dup('c').reshape(expExpandShape);
String msg = "expandDim=" + i + ", source=" + p.getSecond();
log.info("Starting: " + msg);
TestCase tc = new TestCase(sd);
tc.testName(msg)
.expectedOutput("out", expOut)
.expectedOutput(expand.name(), expExpand);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(error);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 12
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testSqueezeGradient() {
val origShape = new long[]{3, 4, 5};
List<String> failed = new ArrayList<>();
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, shape, DataType.DOUBLE)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.squeeze(in, i);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", squeeze, true);
long[] expShapePostSqueeze;
switch (i) {
case 0:
expShapePostSqueeze = new long[]{4, 5};
break;
case 1:
expShapePostSqueeze = new long[]{3, 5};
break;
case 2:
expShapePostSqueeze = new long[]{3, 4};
break;
default:
throw new RuntimeException();
}
INDArray exp = inArr.dup('c').reshape('c', expShapePostSqueeze);
Map<String,INDArray> m = sd.outputAll(null);
INDArray squeezed = m.get(squeeze.name());
// assertArrayEquals(expShapePostSqueeze, squeezed.shape());
INDArray out = m.get(stdev.name());
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
assertEquals(expOut, out);
String msg = "squeezeDim=" + i + ", source=" + p.getSecond();
TestCase tc = new TestCase(sd)
.testName(msg)
.expected(squeeze.name(), exp)
.expectedOutput("out", expOut);
String error = OpValidation.validate(tc, true);
if(error != null){
failed.add(name);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
