piiswrong commented on a change in pull request #9688: bilinear upsample from 
PyTorch
URL: https://github.com/apache/incubator-mxnet/pull/9688#discussion_r165805994
 
 

 ##########
 File path: src/operator/bilinear_upsample.cc
 ##########
 @@ -0,0 +1,185 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://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.
+ */
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file bilinear_upsample.cc
+ * \brief bilinear upsample operator
+ * \author Hang Zhang
+ * Adapted from PyTorch
+*/
+#include "devicetensor.h"
+#include "bilinear_upsample-inl.h"
+#include "elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+
+template<typename xpu, typename DType, typename AccReal>
+void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream<cpu> *s,
+                                           const std::vector<TBlob> &input,
+                                           const std::vector<TBlob> &output) {
+  DeviceTensor<DType, 4> itensor = devicetensor<DType, 4>(input[0]);
+  DeviceTensor<DType, 4> otensor = devicetensor<DType, 4>(output[0]);
+  int nbatch = otensor.getSize(0);
+  int channels = otensor.getSize(1);
+  int outputHeight = otensor.getSize(2);
+  int outputWidth = otensor.getSize(3);
+  int inputHeight = itensor.getSize(2);
+  int inputWidth = itensor.getSize(3);
+
+  DType *idata = itensor.data_ptr();
+  DType *odata = otensor.data_ptr();
+  channels = nbatch * channels;
+  // special case: just copy
+  if (inputHeight == outputHeight && inputWidth == outputWidth) {
+    for (int h2 = 0; h2 < outputHeight; ++h2) {
+      const int h1 = h2;
+      for (int w2 = 0; w2 < outputWidth; ++w2) {
+        const int w1 = w2;
+        const DType* pos1 = &idata[h1 * inputWidth + w1];
+        DType* pos2 = &odata[h2 * outputWidth + w2];
+        for (int c = 0; c < channels; ++c) {
+          pos2[0] = pos1[0];
+          pos1 += inputWidth * inputHeight;
+          pos2 += outputWidth * outputHeight;
+        }
+      }
+    }
+    return;
+  }
+  const float rheight =(outputHeight > 1) ? (float)(inputHeight - 
1)/(outputHeight - 1) : 0.f;
+  const float rwidth = (outputWidth > 1) ? (float)(inputWidth - 1) / 
(outputWidth - 1) : 0.f;
+  for (int h2 = 0; h2 < outputHeight; ++h2) {
+    const float h1r = rheight * h2;
+    const int h1 = h1r;
+    const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
+    const DType h1lambda = h1r - h1;
+    const DType h0lambda = (DType)1. - h1lambda;
+    for (int w2 = 0; w2 < outputWidth; ++w2) {
+      const float w1r = rwidth * w2;
+      const int w1 = w1r;
+      const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
+      const DType w1lambda = w1r - w1;
+      const DType w0lambda = (DType)1. - w1lambda;
+      const DType* pos1 = &idata[h1 * inputWidth + w1];
+      DType* pos2 = &odata[h2 * outputWidth + w2];
+      for (int c = 0; c < channels; ++c) {
+        pos2[0] = h0lambda * (w0lambda * pos1[0]+ w1lambda * pos1[w1p])
+                  + h1lambda * (w0lambda * pos1[h1p * inputWidth]
+                  + w1lambda * pos1[h1p * inputWidth + w1p]);
+        pos1 += inputWidth * inputHeight;
+        pos2 += outputWidth * outputHeight;
+      }
+    }
+  }
+}
+
+
+template<typename xpu, typename DType, typename AccReal>
+void SpatialUpSamplingBilinearUpdateGradInput(mshadow::Stream<cpu> *s,
+                                              const std::vector<TBlob> &input,
+                                              const std::vector<TBlob> 
&output) {
+  DeviceTensor<DType, 4> gradOutput = devicetensor<DType, 4>(input[0]);
+  DeviceTensor<DType, 4> gradInput = devicetensor<DType, 4>(output[0]);
+  int nbatch = gradInput.getSize(0);
+  int channels = gradInput.getSize(1);
+  int outputHeight = gradInput.getSize(2);
+  int outputWidth = gradInput.getSize(3);
+  int inputHeight = gradOutput.getSize(2);
+  int inputWidth = gradOutput.getSize(3);
+
+  DType *data1 = gradInput.data_ptr();
+  DType *data2 = gradOutput.data_ptr();
+  channels = nbatch * channels;
+
+  // special case: same-size matching grids
 
 Review comment:
   This should be handled at the top level by passing through to identity 
compute function

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