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Add im2col and col2im operator (#16502)
* add im2col * add col2im * fix typo * add docs * add unittest * more tests * fix lint * fix doc * fix request * trigger CI
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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. | ||
*/ | ||
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/*! | ||
* Copyright (c) 2015 by Contributors | ||
* \file im2col-inl.h | ||
* \brief | ||
* \author Jiajun Wang | ||
*/ | ||
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#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" | ||
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namespace mxnet { | ||
namespace op { | ||
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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."); | ||
} | ||
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index_t DilatedKernelSize(int dim) const { | ||
return 1 + (kernel[dim] - 1) * dilate[dim]; | ||
} | ||
}; // struct Im2colParam | ||
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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]; | ||
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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; | ||
} | ||
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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); | ||
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if (req[0] == kNullOp) return; | ||
if (req[0] != kAddTo) { | ||
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_); | ||
} | ||
} else { | ||
Tensor<xpu, 2, DType> tcol = ctx.requested[0] | ||
.get_space_typed<xpu, 2, DType>(Shape2(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, tcol.dptr_); | ||
Tensor<xpu, 2, DType> ocol = col[n]; | ||
ocol += tcol; | ||
} | ||
} | ||
}); | ||
} | ||
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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>(); | ||
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const mxnet::TShape im_shape = outputs[0].shape_; | ||
const mxnet::TShape col_shape = inputs[0].shape_; | ||
const index_t num = im_shape[0]; | ||
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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; | ||
} | ||
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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); | ||
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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]); | ||
} | ||
}); | ||
} | ||
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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."); | ||
} | ||
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index_t DilatedKernelSize(int dim) const { | ||
return 1 + (kernel[dim] - 1) * dilate[dim]; | ||
} | ||
}; // struct Col2imParam | ||
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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]; | ||
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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; | ||
} | ||
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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); | ||
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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_, req[0]); | ||
} | ||
}); | ||
} | ||
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template<typename xpu> | ||
void Col2imGradCompute(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>(); | ||
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const mxnet::TShape im_shape = inputs[0].shape_; | ||
const mxnet::TShape col_shape = outputs[0].shape_; | ||
const index_t batch_size = im_shape[0]; | ||
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const int spatial_size = param.kernel.ndim(); | ||
mxnet::TShape col_buffer_shape(1 + spatial_size, 1); | ||
col_buffer_shape[0] = im_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; | ||
} | ||
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MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, { | ||
Tensor<xpu, 4, DType> im_grad = 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_grad = outputs[0].get_with_shape<xpu, 3, DType>( | ||
Shape3(col_shape[0], col_shape[1], col_shape[2]), s); | ||
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if (req[0] == kNullOp) return; | ||
if (req[0] != kAddTo) { | ||
for (index_t n = 0; n < batch_size; ++n) { | ||
im2col(s, im_grad[n].dptr_, im_shape, col_buffer_shape, | ||
param.kernel, param.pad, param.stride, param.dilate, col_grad[n].dptr_); | ||
} | ||
} else { | ||
Tensor<xpu, 2, DType> tgrad = ctx.requested[0] | ||
.get_space_typed<xpu, 2, DType>(Shape2(col_shape[1], col_shape[2]), s); | ||
for (index_t n = 0; n < batch_size; ++n) { | ||
im2col(s, im_grad[n].dptr_, im_shape, col_buffer_shape, | ||
param.kernel, param.pad, param.stride, param.dilate, tgrad.dptr_); | ||
Tensor<xpu, 2, DType> cgrad = col_grad[n]; | ||
cgrad += tgrad; | ||
} | ||
} | ||
}); | ||
} | ||
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} // namespace op | ||
} // namespace mxnet | ||
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#endif // MXNET_OPERATOR_NN_IM2COL_INL_H_ |
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