[GitHub] [incubator-mxnet] arcadiaphy commented on a change in pull request #16502: Add im2col and col2im operator

[GitBox <gi...@apache.org>]

arcadiaphy commented on a change in pull request #16502: Add im2col and col2im operator
URL: https://github.com/apache/incubator-mxnet/pull/16502#discussion_r358605760
 
 

 ##########
 File path: src/operator/nn/im2col-inl.h
 ##########
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+/*
+ * 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) 2015 by Contributors
+ * \file im2col-inl.h
+ * \brief
+ * \author Jiajun Wang
+*/
+
+#ifndef MXNET_OPERATOR_NN_IM2COL_INL_H_
+#define MXNET_OPERATOR_NN_IM2COL_INL_H_
+#include <vector>
+#include "../mxnet_op.h"
+#include "../mshadow_op.h"
+#include "../elemwise_op_common.h"
+#include "./im2col.h"
+
+namespace mxnet {
+namespace op {
+
+struct Im2colParam : public dmlc::Parameter<Im2colParam> {
+  mxnet::TShape kernel;
+  mxnet::TShape stride;
+  mxnet::TShape dilate;
+  mxnet::TShape pad;
+  DMLC_DECLARE_PARAMETER(Im2colParam) {
+    DMLC_DECLARE_FIELD(kernel).describe("Sliding kernel size: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(stride).set_default(mxnet::TShape(0, 0))
+    .describe("The stride between adjacent sliding blocks in spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(dilate).set_default(mxnet::TShape(0, 0))
+    .describe("The spacing between adjacent kernel points: (w,), (h, w) or (d, h, w). "
+              "Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(pad).set_default(mxnet::TShape(0, 0))
+    .describe("The zero-value padding size on both sides of spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to no padding.");
+  }
+
+  index_t DilatedKernelSize(int dim) const {
+    return 1 + (kernel[dim] - 1) * dilate[dim];
+  }
+};  // struct Im2colParam
+
+
+template<typename xpu>
+void Im2colCompute(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Im2colParam& param = nnvm::get<Im2colParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TShape im_shape = inputs[0].shape_;
+  const mxnet::TShape col_shape = outputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im = inputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col = outputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    for (index_t n = 0; n < num; ++n) {
+      im2col(s, im[n].dptr_, im_shape, col_buffer_shape,
+             param.kernel, param.pad, param.stride, param.dilate, col[n].dptr_);
+    }
+  });
+}
+
+template<typename xpu>
+void Im2colGradCompute(const nnvm::NodeAttrs& attrs,
+                       const OpContext& ctx,
+                       const std::vector<TBlob>& inputs,
+                       const std::vector<OpReqType>& req,
+                       const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Im2colParam& param = nnvm::get<Im2colParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+
+  const mxnet::TShape im_shape = outputs[0].shape_;
+  const mxnet::TShape col_shape = inputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im_grad = outputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col_grad = inputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    for (index_t n = 0; n < num; ++n) {
+      col2im(s, col_grad[n].dptr_, im_shape, col_buffer_shape,
+             param.kernel, param.pad, param.stride, param.dilate,
+             im_grad[n].dptr_, req[0]);
+    }
+  });
+}
+
+struct Col2imParam : public dmlc::Parameter<Col2imParam> {
+  mxnet::TShape output_size;
+  mxnet::TShape kernel;
+  mxnet::TShape stride;
+  mxnet::TShape dilate;
+  mxnet::TShape pad;
+  DMLC_DECLARE_PARAMETER(Col2imParam) {
+    DMLC_DECLARE_FIELD(output_size)
+    .describe("The spatial dimension of image array: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(kernel).describe("Sliding kernel size: (w,), (h, w) or (d, h, w).");
+    DMLC_DECLARE_FIELD(stride).set_default(mxnet::TShape(0, 0))
+    .describe("The stride between adjacent sliding blocks in spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(dilate).set_default(mxnet::TShape(0, 0))
+    .describe("The spacing between adjacent kernel points: (w,), (h, w) or (d, h, w). "
+              "Defaults to 1 for each dimension.");
+    DMLC_DECLARE_FIELD(pad).set_default(mxnet::TShape(0, 0))
+    .describe("The zero-value padding size on both sides of spatial dimension: "
+              "(w,), (h, w) or (d, h, w). Defaults to no padding.");
+  }
+
+  index_t DilatedKernelSize(int dim) const {
+    return 1 + (kernel[dim] - 1) * dilate[dim];
+  }
+};  // struct Col2imParam
+
+template<typename xpu>
+void Col2imCompute(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<TBlob>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  const Col2imParam& param = nnvm::get<Col2imParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const mxnet::TShape im_shape = outputs[0].shape_;
+  const mxnet::TShape col_shape = inputs[0].shape_;
+  const index_t num = im_shape[0];
+
+  const int spatial_size = param.kernel.ndim();
+  mxnet::TShape col_buffer_shape(1 + spatial_size, 1);
+  col_buffer_shape[0] = col_shape[1];
+  for (int i = 0; i < spatial_size; ++i) {
+    const index_t pad_size = im_shape[i + 2] + 2 * param.pad[i];
+    const index_t output_size = (pad_size - param.DilatedKernelSize(i)) / param.stride[i] + 1;
+    col_buffer_shape[i + 1] = output_size;
+  }
+
+  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    Tensor<xpu, 4, DType> im = outputs[0].get_with_shape<xpu, 4, DType>(
+        Shape4(im_shape[0], im_shape[1], im_shape[2], im_shape[3]), s);
+    Tensor<xpu, 3, DType> col = inputs[0].get_with_shape<xpu, 3, DType>(
+        Shape3(col_shape[0], col_shape[1], col_shape[2]), s);
+
+    for (index_t n = 0; n < num; ++n) {
+      col2im(s, col[n].dptr_, im_shape, col_buffer_shape,
+             param.kernel, param.pad, param.stride, param.dilate,
+             im[n].dptr_, kWriteTo);
 
 Review comment:
   All fixed. Req types are checked in forward function, too.

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