diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp index 6d24827e5..ea16b2274 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp @@ -52,12 +52,11 @@ namespace nd4j { if (block.getIArguments() && block.getIArguments()->size()) numBits = INT_ARG(0); bool narrowed = false; - //INT_ARG(1); - if (block.getIArguments()->size() == 2) { - numBits = INT_ARG(0); - narrowed = INT_ARG(1); - REQUIRE_TRUE(numBits > 1 && numBits < 17, 0, "fake_quant_with_min_max_vars: Number of bits for quatization should be in between 2 and 16, but %i was given.", numBits); + if (block.getBArguments() && block.getBArguments()->size()) { + narrowed = B_ARG(0); } + REQUIRE_TRUE(numBits > 1 && numBits < 17, 0, "fake_quant_with_min_max_vars: Number of \ + bits for quantization should be in between 2 and 16, but %i was given.", numBits); helpers::fakeQuantWithMinMaxVars(x, min, max, numBits, narrowed, output); return ND4J_STATUS_OK; } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java index 7f59d24e4..b0fc00bac 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java @@ -2612,8 +2612,9 @@ public class DifferentialFunctionFactory { return new DrawBoundingBoxes(sameDiff, boxes, colors).outputVariable(); } - public SDVariable fakeQuantWithMinMaxVarsPerChannel(SDVariable x, SDVariable min, SDVariable max) { - return new FakeQuantWithMinMaxVarsPerChannel(sameDiff,x,min,max).outputVariable(); + public SDVariable fakeQuantWithMinMaxVarsPerChannel(SDVariable x, SDVariable min, SDVariable max, + int num_bits, boolean narrow) { + return new FakeQuantWithMinMaxVarsPerChannel(sameDiff,x,min,max,num_bits,narrow).outputVariable(); } public SDVariable betainc( SDVariable a, SDVariable b, SDVariable x) { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java index 3c6b969b8..cb63dab61 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java @@ -87,6 +87,7 @@ public class ImportClassMapping { org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression.class, org.nd4j.linalg.api.ops.impl.image.NonMaxSuppressionV3.class, org.nd4j.linalg.api.ops.impl.image.ResizeBilinear.class, + org.nd4j.linalg.api.ops.impl.image.ResizeBicubic.class, org.nd4j.linalg.api.ops.impl.image.ResizeNearestNeighbor.class, org.nd4j.linalg.api.ops.impl.indexaccum.FirstIndex.class, org.nd4j.linalg.api.ops.impl.indexaccum.IAMax.class, diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java index c63cd3b56..d46529a84 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java @@ -21,30 +21,46 @@ import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; +import org.tensorflow.framework.AttrValue; +import org.tensorflow.framework.GraphDef; +import org.tensorflow.framework.NodeDef; import java.util.Collections; import java.util.List; +import java.util.Map; public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp { + protected boolean narrowRange; + protected int numBits; + public FakeQuantWithMinMaxVarsPerChannel() {} - public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max) { + public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow) { Preconditions.checkArgument(min.isVector() && max.isVector() && min.length() == max.length(), "FakeQuantWithMinMaxVarsPerChannel: min and max should be 1D tensors with the same length"); - inputArguments.add(x); - inputArguments.add(min); - inputArguments.add(max); + addInputArgument(x,min,max); + addIArgument(num_bits); + addBArgument(narrow); } - public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, - INDArray output) { - this(x,min,max); - outputArguments.add(output); + public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits) { + this(x, min, max, num_bits, false); } - public FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff, SDVariable x, SDVariable min, SDVariable max) { + public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, boolean narrow) { + this(x, min, max, 8, narrow); + } + + public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max) { + this(x, min, max, 8, false); + } + + public FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff, SDVariable x, SDVariable min, SDVariable max, + int num_bits, boolean narrow) { super("", sameDiff, new SDVariable[]{x, min, max}); + addIArgument(num_bits); + addBArgument(narrow); } @Override @@ -57,6 +73,18 @@ public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp { return "FakeQuantWithMinMaxVarsPerChannel"; } + @Override + public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) { + if(attributesForNode.containsKey("narrow_range")){ + this.narrowRange = attributesForNode.get("narrow_range").getB(); + } + if(attributesForNode.containsKey("num_bits")) { + this.numBits = (int) attributesForNode.get("num_bits").getI(); + } + addIArgument(numBits); + addBArgument(narrowRange); + } + @Override public List calculateOutputDataTypes(List inputDataTypes){ Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 inputs, got %s", inputDataTypes); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ResizeBicubic.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ResizeBicubic.java new file mode 100644 index 000000000..18cb15617 --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ResizeBicubic.java @@ -0,0 +1,82 @@ +/******************************************************************************* + * Copyright (c) 2019 Konduit, K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.nd4j.linalg.api.ops.impl.image; + +import lombok.NoArgsConstructor; +import lombok.NonNull; +import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.base.Preconditions; +import org.nd4j.imports.graphmapper.tf.TFGraphMapper; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.api.ops.DynamicCustomOp; +import org.nd4j.linalg.factory.Nd4j; +import org.tensorflow.framework.AttrValue; +import org.tensorflow.framework.GraphDef; +import org.tensorflow.framework.NodeDef; + +import java.util.Collections; +import java.util.List; +import java.util.Map; + +/** + * ResizeBicubic op wrapper + * @author Alexander Stoyakin + */ +@NoArgsConstructor +public class ResizeBicubic extends DynamicCustomOp { + + protected boolean alignCorners = false; + protected boolean alignPixelCenters = false; + + public ResizeBicubic(@NonNull INDArray image, INDArray size, boolean alignCorners, boolean alignPixelCenters) { + addInputArgument(image, size); + addBArgument(alignCorners, alignPixelCenters); + } + + public ResizeBicubic(@NonNull SameDiff sameDiff, @NonNull SDVariable image, + SDVariable size, boolean alignCorners, boolean alignPixelCenters) { + super(sameDiff, new SDVariable[]{image, size}); + addBArgument(alignCorners, alignPixelCenters); + } + + @Override + public String opName() { + return "resize_bicubic"; + } + + @Override + public String tensorflowName() { + return "ResizeBicubic"; + } + + @Override + public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) { + TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph); + + this.alignCorners = attributesForNode.get("align_corners").getB(); + this.alignPixelCenters = attributesForNode.get("half_pixel_centers").getB(); + addBArgument(alignCorners, alignPixelCenters); + } + + @Override + public List calculateOutputDataTypes(List inputDataTypes){ + Preconditions.checkState(inputDataTypes != null && (inputDataTypes.size() == 1 || inputDataTypes.size() == 2), + "Expected 1 or 2 input datatypes for %s, got %s", getClass(), inputDataTypes); + return Collections.singletonList(Nd4j.defaultFloatingPointType()); + } +} diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxArgs.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxArgs.java index 8aeb26b48..5186fda30 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxArgs.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxArgs.java @@ -4,6 +4,7 @@ import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.tensorflow.framework.AttrValue; import org.tensorflow.framework.GraphDef; @@ -37,11 +38,21 @@ public class FakeQuantWithMinMaxArgs extends DynamicCustomOp { addArgs(); } + public FakeQuantWithMinMaxArgs(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow) { + Preconditions.checkArgument(min.isVector() && max.isVector() && + min.length() == max.length(), + "FakeQuantWithMinMaxArgs: min and max should be 1D tensors with the same length"); + addInputArgument(x,min,max); + addIArgument(num_bits); + addBArgument(narrow); + } + public FakeQuantWithMinMaxArgs(){ } protected void addArgs(){ iArguments.clear(); - addIArgument(numBits, narrowRange ? 1 : 0); + addIArgument(numBits); + addBArgument(narrowRange); addTArgument(min, max); } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxVars.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxVars.java index bf09ae88c..efe7d71a1 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxVars.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/FakeQuantWithMinMaxVars.java @@ -4,6 +4,7 @@ import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.tensorflow.framework.AttrValue; import org.tensorflow.framework.GraphDef; @@ -33,11 +34,22 @@ public class FakeQuantWithMinMaxVars extends DynamicCustomOp { addArgs(); } + public FakeQuantWithMinMaxVars(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow) { + Preconditions.checkArgument(min.isVector() && max.isVector() && + min.length() == max.length(), + "FakeQuantWithMinMaxVars: min and max should be 1D tensors with the same length"); + addInputArgument(x,min,max); + addIArgument(num_bits); + addBArgument(narrow); + } + public FakeQuantWithMinMaxVars(){ } protected void addArgs(){ iArguments.clear(); - addIArgument(numBits, narrowRange ? 1 : 0); + bArguments.clear(); + addIArgument(numBits); + addBArgument(narrowRange); } @Override diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/DivOp.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/DivOp.java index b76942e95..5273a2941 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/DivOp.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/DivOp.java @@ -57,8 +57,8 @@ public class DivOp extends BaseDynamicTransformOp { } @Override - public String tensorflowName() { - return "Div"; + public String[] tensorflowNames() { + return new String[]{"Div","RealDiv"}; } diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java index 9f67ac49a..9e3db5b1a 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java @@ -111,29 +111,17 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a // 2019/11/15 - missing dtype argument in nd4j, tests are useless https://github.com/eclipse/deeplearning4j/issues/8398 "zeros_like/rank2_float32_dtype_int.*", - // 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8402 - "fake_quant/min_max_args_per_channel.*", - - // Suggesting TF 1.15 bug - "non_max_suppression_v2/float16.*", - // 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8450 "betainc.*", - // 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8452 - "polygamma.*", - // 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8453 "roll/.*", // 11.26.2019 failing https://github.com/eclipse/deeplearning4j/issues/8455 "matrix_band_part/.*", - // 11.28.2019 failing https://github.com/eclipse/deeplearning4j/issues/8458 - "adjust_hue/.*", - - // 11.28.2019 failing https://github.com/eclipse/deeplearning4j/issues/8459 - "adjust_saturation/.*" + // 05.12.2019 failing https://github.com/eclipse/deeplearning4j/issues/8507 + "resize_bicubic/int32.*" }; /* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java index 742ffae66..c3d4fe699 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java @@ -943,16 +943,9 @@ public class CustomOpsTests extends BaseNd4jTest { 0.0877f, 0.5966f, 0.6600f, 0.3513f, 0.1604f}).reshape(3,5); INDArray out = Nd4j.createUninitialized(x.shape()); - val op = new FakeQuantWithMinMaxVarsPerChannel(x,min,max,out); + val op = new FakeQuantWithMinMaxVarsPerChannel(x,min,max); Nd4j.exec(op); assertEquals(expected, out); - - /*TF: [[ 0.7801, 0.5966, 0.7260, 0.2320, 0.5084], - [ 0.1800, 0.5046, 0.8684, 0.3513, 0.5084], - [ 0.0877, 0.5966, 0.6600, 0.3513, 0.1604]] - SD: [[ 0.7770, 0.5969, 0.7232, 0.2310, 0.5098], - [ 0.1793, 0.5053, 0.8685, 0.3500, 0.5098], - [ 0.0874, 0.5969, 0.6574, 0.3500, 0.1597]]*/ } @Test @@ -1036,13 +1029,12 @@ public class CustomOpsTests extends BaseNd4jTest { INDArray min = Nd4j.createFromArray(new float[]{-63.65f}); INDArray max = Nd4j.createFromArray(new float[]{0.1f}); - INDArray output = Nd4j.createUninitialized(DataType.FLOAT, 1,2,3,1); INDArray expected = Nd4j.createFromArray(new float[]{-63.75f, -63.75f, -63.5f, -63.5f, 0.f, 0.f}). reshape(1,2,3,1); - Nd4j.exec(new FakeQuantWithMinMaxVarsPerChannel(x,min,max,output)); + INDArray[] output = Nd4j.exec(new FakeQuantWithMinMaxVarsPerChannel(x,min,max)); - assertEquals(expected, output); + assertEquals(expected, output[0]); } @Test