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 +1,77 @@
+/* ******************************************************************************
+ *
+ *
+ * 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 sgazeos@gmail.com
+//
+#include <ops/declarable/helpers/random_crop.h>
+#include <graph/Context.h>
+
+#if NOT_EXCLUDED(OP_random_shuffle)
+namespace sd {
+namespace ops {
+namespace helpers {
+
+template <typename T>
+static sd::Status _randomCropFunctor(graph::Context& context, NDArray* input, NDArray* shape, NDArray* output,
+                                     int seed) {
+  graph::RandomGenerator rngX(context.getRng());
+  // functions::random::RandomFunction<T>::template execTransform<randomOps::UniformDistribution<T>>(rng,
+  // output->buffer(), output->shapeInfo(), std::vector<T>({T(0.), shape->e(last)}).data());
+  // NativeOpExecutioner::execRandom(random::UniformDistribution, rng, output->buffer(), output->shapeInfo(),
+  // std::vector<T>({T(0.), shape->e<T>(last)}).data());
+  sd::LongType last = shape->lengthOf() - 1;
+
+  rngX.setSeed(seed);
+  // functions::random::RandomFunction<T>::template execTransform<randomOps::UniformDistribution<T>>(rng,
+  // output->buffer(), output->shapeInfo(), std::vector<T>({T(0.), shape->getScalar(last)}).data());
+  for (sd::LongType e = 0; e < output->lengthOf(); ++e) {
+    T put = rngX.relativeT<T>(e, static_cast<T>(0), static_cast<T>(shape->e<sd::LongType>(last)));
+    output->p(e, put);
+  }
+  sd::LongType maxIndex = output->argMax();
+  sd::LongType startPos = output->e<sd::LongType>(maxIndex);
+  sd::LongType lastDim = input->sizeAt(-1);
+  sd::LongType pos = 0;
+  sd::LongType width = startPos + shape->e<sd::LongType>(last);
+  if (width >= lastDim) {
+    startPos -= (width - lastDim);
+    width = lastDim;
+  }
+
+  for (sd::LongType i = 0; i < input->lengthOf(); i += lastDim) {
+    for (sd::LongType k = startPos; k < width && pos < output->lengthOf(); k++) {
+      output->p(pos++, input->e<T>(i + k));
+    }
+  }
+  return sd::Status::OK;
+}
+
+sd::Status randomCropFunctor(graph::Context& context, NDArray* input, NDArray* shape, NDArray* output, int seed) {
+  BUILD_SINGLE_SELECTOR(input->dataType(), return _randomCropFunctor, (context, input, shape, output, seed),
+                        SD_FLOAT_TYPES);
+}
+
+BUILD_SINGLE_TEMPLATE( sd::Status _randomCropFunctor,
+                      (graph::Context & context, NDArray* input, NDArray* shape, NDArray* output, int seed),
+                      SD_FLOAT_TYPES);
+
+}  // namespace helpers
+}  // namespace ops
+}  // namespace sd
+#endif