Java Code Examples for org.nd4j.linalg.factory.Nd4j#alloc()
The following examples show how to use
org.nd4j.linalg.factory.Nd4j#alloc() .
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Example 1
| Source File: DoubleDataBufferTest.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSerialization() {
DataBuffer buf = Nd4j.createBuffer(5);
String fileName = "buf.ser";
File file = new File(fileName);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
DataBuffer buf2 = SerializationUtils.readObject(file);
//assertEquals(buf, buf2);
assertArrayEquals(buf.asDouble(), buf2.asDouble(), 0.001);
Nd4j.alloc = DataBuffer.AllocationMode.DIRECT;
buf = Nd4j.createBuffer(5);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
buf2 = SerializationUtils.readObject(file);
// assertEquals(buf, buf2);
assertArrayEquals(buf.asDouble(), buf2.asDouble(), 0.001);
}
Example 2
| Source File: FloatDataBufferTest.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSerialization() {
DataBuffer buf = Nd4j.createBuffer(5);
String fileName = "buf.ser";
File file = new File(fileName);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
DataBuffer buf2 = SerializationUtils.readObject(file);
// assertEquals(buf, buf2);
assertArrayEquals(buf.asFloat(), buf2.asFloat(), 0.0001f);
Nd4j.alloc = DataBuffer.AllocationMode.DIRECT;
buf = Nd4j.createBuffer(5);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
buf2 = SerializationUtils.readObject(file);
//assertEquals(buf, buf2);
assertArrayEquals(buf.asFloat(), buf2.asFloat(), 0.0001f);
}
Example 3
| Source File: DoubleDataBufferTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSerialization() throws Exception {
File dir = testDir.newFolder();
DataBuffer buf = Nd4j.createBuffer(5);
String fileName = "buf.ser";
File file = new File(dir, fileName);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
DataBuffer buf2 = SerializationUtils.readObject(file);
//assertEquals(buf, buf2);
assertArrayEquals(buf.asDouble(), buf2.asDouble(), 0.001);
Nd4j.alloc = DataBuffer.AllocationMode.DIRECT;
buf = Nd4j.createBuffer(5);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
buf2 = SerializationUtils.readObject(file);
// assertEquals(buf, buf2);
assertArrayEquals(buf.asDouble(), buf2.asDouble(), 0.001);
}
Example 4
| Source File: FloatDataBufferTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSerialization() throws Exception {
File dir = testDir.newFolder();
DataBuffer buf = Nd4j.createBuffer(5);
String fileName = "buf.ser";
File file = new File(dir, fileName);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
DataBuffer buf2 = SerializationUtils.readObject(file);
// assertEquals(buf, buf2);
assertArrayEquals(buf.asFloat(), buf2.asFloat(), 0.0001f);
Nd4j.alloc = DataBuffer.AllocationMode.DIRECT;
buf = Nd4j.createBuffer(5);
file.deleteOnExit();
SerializationUtils.saveObject(buf, file);
buf2 = SerializationUtils.readObject(file);
//assertEquals(buf, buf2);
assertArrayEquals(buf.asFloat(), buf2.asFloat(), 0.0001f);
}
Example 5
| Source File: ConvolutionTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataBuffer.Type[] types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE,
DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataBuffer.Type initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataBuffer.Type type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pw)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertArrayEquals(outOrig.data().asFloat(),
outNew.data().asFloat(), 0.01f);
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
Example 6
| Source File: ConvolutionTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2Col() throws Exception {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
DataBuffer.Type[] types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE,
DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataBuffer.Type inititalType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataBuffer.Type type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
System.out.println("Before assertion");
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pw)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
assertEquals(in.data().allocationMode(), mode);
assertEquals(in.data().dataType(), type);
INDArray im2col = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
false); //Cheating, to get correct shape for input
INDArray imgOutOld =
OldConvolution.col2im(im2col, sh, sw, ph, pw, h, w);
INDArray imgOutNew =
Convolution.col2im(im2col, sh, sw, ph, pw, h, w);
System.out.println("F order test");
System.out.println(imgOutOld);
System.out.println(imgOutNew);
assertEquals(imgOutOld, imgOutNew);
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(inititalType);
}
Example 7
| Source File: ConvolutionTestsC.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataBuffer.Type[] types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE,
DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataBuffer.Type initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataBuffer.Type type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pw)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
Example 8
| Source File: ConvolutionTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataType[] types = new DataType[] {DataType.FLOAT, DataType.FLOAT,
DataType.FLOAT, DataType.FLOAT};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataType[] {DataType.FLOAT, DataType.FLOAT};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataType initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataType type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pW)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertArrayEquals(outOrig.data().asFloat(),
outNew.data().asFloat(), 0.01f);
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
Example 9
| Source File: ConvolutionTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2Col() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
DataType[] types = new DataType[] {DataType.FLOAT, DataType.FLOAT,
DataType.FLOAT, DataType.FLOAT};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataType[] {DataType.FLOAT, DataType.FLOAT};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataType inititalType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataType type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
System.out.println("Before assertion");
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pW)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
assertEquals(in.data().allocationMode(), mode);
assertEquals(in.data().dataType(), type);
INDArray im2col = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
false); //Cheating, to get correct shape for input
INDArray imgOutOld =
OldConvolution.col2im(im2col, sh, sw, ph, pw, h, w);
INDArray imgOutNew =
Convolution.col2im(im2col, sh, sw, ph, pw, h, w);
System.out.println("F order test");
System.out.println(imgOutOld);
System.out.println(imgOutNew);
assertEquals(imgOutOld, imgOutNew);
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(inititalType);
}
Example 10
| Source File: ConvolutionTestsC.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataType[] types = new DataType[] {DataType.FLOAT, DataType.DOUBLE,
DataType.FLOAT, DataType.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataType[] {DataType.FLOAT, DataType.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataType initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataType type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pW)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
