#include <vector>

#include "caffe/layers/im2col_layer.hpp"

#include "caffe/util/im2col.hpp"

namespace caffe {

template <typename Dtype>

void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top) {

ConvolutionParameter conv_param = this->layer_param_.convolution_param();

force_nd_im2col_ = conv_param.force_nd_im2col();

const int_tp input_num_dims = bottom[0]->shape().size();

channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());

const int_tp first_spatial_dim = channel_axis_ + 1;

num_spatial_axes_ = input_num_dims - first_spatial_dim;

CHECK_GE(num_spatial_axes_, 1);

vector<int_tp> dim_blob_shape(1, num_spatial_axes_);

// Setup filter kernel dimensions (kernel_shape_).

kernel_shape_.Reshape(dim_blob_shape);

int_tp* kernel_shape_data = kernel_shape_.mutable_cpu_data();

if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {

CHECK_EQ(num_spatial_axes_, 2)

<< "kernel_h & kernel_w can only be used for 2D convolution.";

CHECK_EQ(0, conv_param.kernel_size_size())

<< "Either kernel_size or kernel_h/w should be specified; not both.";

kernel_shape_data[0] = conv_param.kernel_h();

kernel_shape_data[1] = conv_param.kernel_w();

} else {

const int_tp num_kernel_dims = conv_param.kernel_size_size();

CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)

<< "kernel_size must be specified once, or once per spatial dimension "

<< "(kernel_size specified " << num_kernel_dims << " times; "

<< num_spatial_axes_ << " spatial dims);";

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

kernel_shape_data[i] =

conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);

}

}

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";

}

// Setup stride dimensions (stride_).

stride_.Reshape(dim_blob_shape);

int_tp* stride_data = stride_.mutable_cpu_data();

if (conv_param.has_stride_h() || conv_param.has_stride_w()) {

CHECK_EQ(num_spatial_axes_, 2)

<< "stride_h & stride_w can only be used for 2D convolution.";

CHECK_EQ(0, conv_param.stride_size())

<< "Either stride or stride_h/w should be specified; not both.";

stride_data[0] = conv_param.stride_h();

stride_data[1] = conv_param.stride_w();

} else {

const int_tp num_stride_dims = conv_param.stride_size();

CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||

num_stride_dims == num_spatial_axes_)

<< "stride must be specified once, or once per spatial dimension "

<< "(stride specified " << num_stride_dims << " times; "

<< num_spatial_axes_ << " spatial dims);";

const int_tp kDefaultStride = 1;

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :

conv_param.stride((num_stride_dims == 1) ? 0 : i);

CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";

}

}

// Setup pad dimensions (pad_).

pad_.Reshape(dim_blob_shape);

int_tp* pad_data = pad_.mutable_cpu_data();

if (conv_param.has_pad_h() || conv_param.has_pad_w()) {

CHECK_EQ(num_spatial_axes_, 2)

<< "pad_h & pad_w can only be used for 2D convolution.";

CHECK_EQ(0, conv_param.pad_size())

<< "Either pad or pad_h/w should be specified; not both.";

pad_data[0] = conv_param.pad_h();

pad_data[1] = conv_param.pad_w();

} else {

const int_tp num_pad_dims = conv_param.pad_size();

CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||

num_pad_dims == num_spatial_axes_)

<< "pad must be specified once, or once per spatial dimension "

<< "(pad specified " << num_pad_dims << " times; "

<< num_spatial_axes_ << " spatial dims);";

const int_tp kDefaultPad = 0;

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :

conv_param.pad((num_pad_dims == 1) ? 0 : i);

}

}

// Setup dilation dimensions (dilation_).

dilation_.Reshape(dim_blob_shape);

int_tp* dilation_data = dilation_.mutable_cpu_data();

const int_tp num_dilation_dims = conv_param.dilation_size();

CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||

num_dilation_dims == num_spatial_axes_)

<< "dilation must be specified once, or once per spatial dimension "

<< "(dilation specified " << num_dilation_dims << " times; "

<< num_spatial_axes_ << " spatial dims).";

const int_tp kDefaultDilation = 1;

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :

conv_param.dilation((num_dilation_dims == 1) ? 0 : i);

}

}

template <typename Dtype>

void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top) {

vector<int_tp> top_shape = bottom[0]->shape();

const int_tp* kernel_shape_data = kernel_shape_.cpu_data();

const int_tp* stride_data = stride_.cpu_data();

const int_tp* pad_data = pad_.cpu_data();

const int_tp* dilation_data = dilation_.cpu_data();

for (int_tp i = 0; i < num_spatial_axes_; ++i) {

top_shape[channel_axis_] *= kernel_shape_data[i];

const int_tp input_dim = bottom[0]->shape(channel_axis_ + i + 1);

const int_tp kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1)

+ 1;

const int_tp output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)

/ stride_data[i] + 1;

top_shape[channel_axis_ + i + 1] = output_dim;

}

top[0]->Reshape(top_shape);

num_ = bottom[0]->count(0, channel_axis_);

bottom_dim_ = bottom[0]->count(channel_axis_);

top_dim_ = top[0]->count(channel_axis_);

channels_ = bottom[0]->shape(channel_axis_);

}

template <typename Dtype>

void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top) {

const Dtype* bottom_data = bottom[0]->cpu_data();

Dtype* top_data = top[0]->mutable_cpu_data();

for (int_tp n = 0; n < num_; ++n) {

DCHECK_EQ(bottom[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);

DCHECK_EQ(top[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);

DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_);

DCHECK_EQ(pad_.count(), num_spatial_axes_);

DCHECK_EQ(stride_.count(), num_spatial_axes_);

DCHECK_EQ(dilation_.count(), num_spatial_axes_);

if (!force_nd_im2col_ && num_spatial_axes_ == 2) {

im2col_cpu(bottom_data + n * bottom_dim_, channels_,

bottom[0]->shape(channel_axis_ + 1),

bottom[0]->shape(channel_axis_ + 2),

kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],

pad_.cpu_data()[0], pad_.cpu_data()[1],

stride_.cpu_data()[0], stride_.cpu_data()[1],

dilation_.cpu_data()[0], dilation_.cpu_data()[1],

top_data + n * top_dim_);

} else {

im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_,

bottom[0]->shape().data() + channel_axis_,

top[0]->shape().data() + channel_axis_,

kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),

dilation_.cpu_data(), top_data + n * top_dim_);

}

}

}

template <typename Dtype>

void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {

const Dtype* top_diff = top[0]->cpu_diff();

Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();

for (int_tp n = 0; n < num_; ++n) {

if (!force_nd_im2col_ && num_spatial_axes_ == 2) {

col2im_cpu(top_diff + n * top_dim_, channels_,

bottom[0]->shape(channel_axis_ + 1),

bottom[0]->shape(channel_axis_ + 2),

kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],

pad_.cpu_data()[0], pad_.cpu_data()[1],

stride_.cpu_data()[0], stride_.cpu_data()[1],

dilation_.cpu_data()[0], dilation_.cpu_data()[1],

bottom_diff + n * bottom_dim_);

} else {

col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_,

bottom[0]->shape().data() + channel_axis_,

top[0]->shape().data() + channel_axis_,

kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),

dilation_.cpu_data(), bottom_diff + n * bottom_dim_);

}

}

}

#ifdef CPU_ONLY

STUB_GPU(Im2colLayer);

#endif

INSTANTIATE_CLASS(Im2colLayer);

REGISTER_LAYER_CLASS(Im2col);

} // namespace caffe