chore: import upstream snapshot with attribution

2026-07-13 12:47:05 +08:00
commit 4f3b7da785
7394 changed files with 2005594 additions and 0 deletions
@@ -0,0 +1,103 @@
+/* ******************************************************************************
+ *
+ *
+ * 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.
+ *
+ *  See the NOTICE file distributed with this work for additional
+ *  information regarding copyright ownership.
+ * 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
+ ******************************************************************************/
+
+//
+// @author @cpuheater
+//
+
+#include <system/op_boilerplate.h>
+#if NOT_EXCLUDED(OP_confusion_matrix)
+
+#include <array/NDArray.h>
+#include <array/NDArrayList.h>
+#include <helpers/ShapeUtils.h>
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/helpers/confusion.h>
+
+#include <array>
+
+namespace sd {
+namespace ops {
+
+CUSTOM_OP_IMPL(confusion_matrix, 2, 1, false, 0, -2) {
+  auto labels = INPUT_VARIABLE(0);
+  auto predictions = INPUT_VARIABLE(1);
+  NDArray *weights = nullptr;
+  if (block.width() > 2) {
+    weights = INPUT_VARIABLE(2);
+    REQUIRE_TRUE(weights->isSameShape(predictions), 0,
+                 "CONFUSION_MATRIX: Weights and predictions should have equal shape");
+  }
+  auto output = OUTPUT_NULLIFIED(0);
+
+  auto* minPredictionArr = predictions->reduceNumber(reduce::Min);
+  int minPrediction = minPredictionArr->e<int>(0);
+  delete minPredictionArr;
+  
+  auto* minLabelArr = labels->reduceNumber(reduce::Min);
+  int minLabel = minLabelArr->e<int>(0);
+  delete minLabelArr;
+
+  REQUIRE_TRUE(minLabel >= 0, 0, "CONFUSION_MATRIX: Labels contains negative values !");
+  REQUIRE_TRUE(minPrediction >= 0, 0, "CONFUSION_MATRIX: Predictions contains negative values !");
+  REQUIRE_TRUE(labels->isVector(), 0, "CONFUSION_MATRIX: Labels input should be a Vector, but got %iD instead",
+               labels->rankOf());
+  REQUIRE_TRUE(predictions->isVector(), 0, "CONFUSION_MATRIX: Predictions input should be Vector, but got %iD instead",
+               predictions->rankOf());
+  REQUIRE_TRUE(labels->isSameShape(predictions), 0, "CONFUSION_MATRIX: Labels and predictions should have equal shape");
+
+  helpers::confusionFunctor(block.launchContext(), labels, predictions, weights, output);
+
+  return sd::Status::OK;
+}
+
+DECLARE_SHAPE_FN(confusion_matrix) {
+  auto labels = INPUT_VARIABLE(0);
+  auto predictions = INPUT_VARIABLE(1);
+  auto dtype = block.numD() ? D_ARG(0) : sd::DataType::INT64;
+  int numClasses = 0;
+
+  if (block.getIArguments()->size() > 0) {
+    numClasses = INT_ARG(0);
+  } else {
+    auto* maxPredictionArr = predictions->reduceNumber(reduce::Max);
+    int maxPrediction = maxPredictionArr->e<int>(0);
+    delete maxPredictionArr;
+    
+    auto* maxLabelArr = labels->reduceNumber(reduce::Max);
+    int maxLabel = maxLabelArr->e<int>(0);
+    delete maxLabelArr;
+    
+    numClasses = (maxPrediction >= maxLabel) ? maxPrediction + 1 : maxLabel + 1;
+  }
+
+  std::array<sd::LongType, 2> shape = {{numClasses, numClasses}};
+  auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(dtype, 'c', 2, shape.data(),0);
+  return SHAPELIST(newShape);
+}
+
+DECLARE_TYPES(confusion_matrix) {
+  getOpDescriptor()
+      ->setAllowedInputTypes({ALL_INDICES})
+      ->setAllowedOutputTypes({ALL_INDICES});
+}
+
+}  // namespace ops
+}  // namespace sd
+
+#endif