void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const int num_axes = bottom[0]->num_axes();
// Setting forced_3d_ in LayerSetup() alone is not sufficient as that can be
// skipped and Reshape() is directed called.
if (num_axes == 5 && channel_axis_ == 1 && bottom[0]->shape(2) == 1) {
forced_3d_ = true;
} else {
forced_3d_ = false;
}
const int first_spatial_axis = channel_axis_ + 1 + forced_3d_;
CHECK_EQ(num_axes, first_spatial_axis + num_spatial_axes_)
<< "bottom num_axes may not change.";
num_ = bottom[0]->count(0, channel_axis_);
CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
<< "Input size incompatible with convolution kernel.";
// TODO: generalize to handle inputs of different shapes.
for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
<< "shape mismatch - bottom[0]: " << bottom[0]->shape_string()
<< " vs. bottom[" << bottom_id << "]: "
<< bottom[bottom_id]->shape_string();
}
// Shape the tops.
bottom_shape_ = &bottom[0]->shape();
compute_output_shape();
vector<int> top_shape(bottom[0]->shape().begin(),
bottom[0]->shape().begin() + channel_axis_);
top_shape.push_back(num_output_);
if (forced_3d_)
top_shape.push_back(1); // in place of length
for (int i = 0; i < num_spatial_axes_; ++i) {
top_shape.push_back(output_shape_[i]);
}
for (int top_id = 0; top_id < top.size(); ++top_id) {
top[top_id]->Reshape(top_shape);
}
if (reverse_dimensions()) {
conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
} else {
conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
}
col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
// Setup input dimensions (conv_input_shape_).
vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
conv_input_shape_.Reshape(bottom_dim_blob_shape);
int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
if (reverse_dimensions()) {
conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i + forced_3d_);
} else {
conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i +
forced_3d_);
}
}
// The im2col result buffer will only hold one image at a time to avoid
// overly large memory usage. In the special case of 1x1 convolution
// it goes lazily unused to save memory.
col_buffer_shape_.clear();
col_buffer_shape_.push_back(kernel_dim_ * group_);
for (int i = 0; i < num_spatial_axes_; ++i) {
if (reverse_dimensions()) {
col_buffer_shape_.push_back(input_shape(i + 1));
} else {
col_buffer_shape_.push_back(output_shape_[i]);
}
}
col_buffer_.Reshape(col_buffer_shape_);
bottom_dim_ = bottom[0]->count(channel_axis_);
top_dim_ = top[0]->count(channel_axis_);
num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
// Set up the all ones "bias multiplier" for adding biases by BLAS
out_spatial_dim_ = top[0]->count(first_spatial_axis);
if (bias_term_) {
vector<int> bias_multiplier_shape(1, out_spatial_dim_);
bias_multiplier_.Reshape(bias_multiplier_shape);
caffe_set(bias_multiplier_.count(), Dtype(1),
bias_multiplier_.mutable_cpu_data());
}
}
void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const int_tp first_spatial_axis = channel_axis_ + 1;
CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_)
<< "bottom num_axes may not change.";
num_ = bottom[0]->count(0, channel_axis_);
CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
<< "Input size incompatible with convolution kernel.";
// TODO: generalize to handle inputs of different shapes.
for (int_tp bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
<< "All inputs must have the same shape.";
}
// Shape the tops.
bottom_shape_ = &bottom[0]->shape();
compute_output_shape();
vector<int_tp> top_shape(bottom[0]->shape().begin(),
bottom[0]->shape().begin() + channel_axis_);
top_shape.push_back(num_output_);
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
top_shape.push_back(output_shape_[i]);
}
for (int_tp top_id = 0; top_id < top.size(); ++top_id) {
top[top_id]->Reshape(top_shape);
}
if (reverse_dimensions()) {
conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
} else {
conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
}
col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
// Setup input dimensions (conv_input_shape_).
vector<int_tp> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
conv_input_shape_.Reshape(bottom_dim_blob_shape);
int_tp* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
for (int_tp i = 0; i < num_spatial_axes_ + 1; ++i) {
if (reverse_dimensions()) {
conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
} else {
conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
}
}
// The im2col result buffer will only hold one image at a time to avoid
// overly large memory usage. In the special case of 1x1 convolution
// it goes lazily unused to save memory.
col_buffer_shape_.clear();
col_buffer_shape_.push_back(kernel_dim_ * group_);
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
if (reverse_dimensions()) {
col_buffer_shape_.push_back(input_shape(i + 1));
} else {
col_buffer_shape_.push_back(output_shape_[i]);
}
}
col_buffer_.Reshape(col_buffer_shape_);
if (Caffe::mode() == Caffe::Brew::GPU) {
// Shared column buffer per device-queue across all layers on that device
for (int_tp i = 0; i < this->device_->num_queues(); ++i) {
shared_ptr<Blob<Dtype> > buffer = this->device_
->template Buffer<Dtype>(i);
buffer->Reshape(col_buffer_shape_);
}
}
bottom_dim_ = bottom[0]->count(channel_axis_);
top_dim_ = top[0]->count(channel_axis_);
num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
// Set up the all ones "bias multiplier" for adding biases by BLAS
out_spatial_dim_ = top[0]->count(first_spatial_axis);
if (bias_term_) {
vector<int_tp> bias_multiplier_shape(1, out_spatial_dim_);
bool reshaped = bias_multiplier_.Reshape(bias_multiplier_shape);
// This will trigger a memory copy if in GPU mode,
// which may not be necessary.
// Thus omit to set the values if not necessary.
if (reshaped) {
caffe_set(bias_multiplier_.count(), Dtype(1),
bias_multiplier_.mutable_cpu_data());
}
}
}
void BaseConvolutionNDLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
const int first_spatial_axis = channel_axis_ + 1;
const int num_axes = bottom[0]->num_axes();
num_spatial_axes_ = num_axes - first_spatial_axis;
CHECK_GE(num_spatial_axes_, 1);
num_ = bottom[0]->count(0, channel_axis_);
CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
<< "Input size incompatible with convolution kernel.";
// TODO: generalize to handle inputs of different shapes.
for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
<< "All inputs must have the same shape.";
}
// Shape the tops.
compute_output_shape();
vector<int> top_shape = bottom[0]->shape();
top_shape[channel_axis_] = num_output_;
top_shape.resize(first_spatial_axis); // Discard input spatial axes.
for (int i = 0; i < num_spatial_axes_; ++i) {
top_shape.push_back(output_shape_[i]);
}
for (int top_id = 0; top_id < top.size(); ++top_id) {
top[top_id]->Reshape(top_shape);
}
if (reverse_dimensions()) {
conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
} else {
conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
}
const int* kernel_shape_data = kernel_shape_.cpu_data();
kernel_dim_ = conv_in_channels_;
for (int i = 0; i < num_spatial_axes_; ++i) {
kernel_dim_ *= kernel_shape_data[i];
}
weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
// Setup input dimensions (conv_input_shape_).
vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
conv_input_shape_.Reshape(bottom_dim_blob_shape);
int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
if (reverse_dimensions()) {
conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
} else {
conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
}
}
// The im2col result buffer will only hold one image at a time to avoid
// overly large memory usage. In the special case of 1x1 convolution
// it goes lazily unused to save memory.
col_buffer_shape_.clear();
col_buffer_shape_.push_back(kernel_dim_);
const int* input_shape_data = input_shape_.cpu_data() + 1;
for (int i = 0; i < num_spatial_axes_; ++i) {
if (reverse_dimensions()) {
col_buffer_shape_.push_back(input_shape_data[i]);
} else {
col_buffer_shape_.push_back(output_shape_[i]);
}
}
col_buffer_.Reshape(col_buffer_shape_);
bottom_dim_ = bottom[0]->count(channel_axis_);
top_dim_ = top[0]->count(channel_axis_);
num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
// Set up the all ones "bias multiplier" for adding biases by BLAS
out_spatial_dim_ = top[0]->count(first_spatial_axis);
if (bias_term_) {
vector<int> bias_multiplier_shape(1, out_spatial_dim_);
bias_multiplier_.Reshape(bias_multiplier_shape);
caffe_set(bias_multiplier_.count(), Dtype(1),
bias_multiplier_.mutable_cpu_data());
}
}
void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// Configure the kernel size, padding, stride, and inputs.
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
force_nd_im2col_ = conv_param.force_nd_im2col();
channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
const int_tp first_spatial_axis = channel_axis_ + 1;
const int_tp num_axes = bottom[0]->num_axes();
num_spatial_axes_ = num_axes - first_spatial_axis;
CHECK_GE(num_spatial_axes_, 0);
vector<int_tp> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
vector<int_tp> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1L));
// Setup filter kernel dimensions (kernel_shape_).
kernel_shape_.Reshape(spatial_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(spatial_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(spatial_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 kernel stride dimensions
kstride_.Reshape(spatial_dim_blob_shape);
int_tp* kstride_data = kstride_.mutable_cpu_data();
if (conv_param.has_kstride_h() || conv_param.has_kstride_w()) {
CHECK_EQ(num_spatial_axes_, 2)
<< "kstride_h & kstride_w can only be used for 2D convolution.";
CHECK_EQ(0, conv_param.kstride_size())
<< "Etiher kstride or kstride_h/w should be specified; not both.";
kstride_data[0] = conv_param.kstride_h();
kstride_data[1] = conv_param.kstride_w();
} else {
const int_tp num_kstride_dims = conv_param.kstride_size();
CHECK(num_kstride_dims == 0 || num_kstride_dims == 1 ||
num_kstride_dims == num_spatial_axes_)
<< "kstride must be specified once, or once per spatial dimension "
<< "(kstride specified " << num_kstride_dims << " times; "
<< num_spatial_axes_ << " spatial dims);";
const int_tp kDefaultKstride = 1;
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
kstride_data[i] = (num_kstride_dims == 0) ? kDefaultKstride :
conv_param.kstride((num_kstride_dims == 1) ? 0 : i);
}
}
// Different 2D and ND im2col/col2im kernels for strided kernels
use_skernel_ = false;
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
use_skernel_ |= (kstride_data[i] != 1);
if (use_skernel_) {
break;
}
}
// Special case: im2col is the identity for 1x1 convolution with stride 1
// and no padding, so flag for skipping the buffer and transformation.
is_1x1_ = true;
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
is_1x1_ &= kernel_shape_data[i] == 1 && stride_data[i] == 1
&& pad_data[i] == 0;
if (!is_1x1_) {
break;
}
}
// Configure output channels and groups.
channels_ = bottom[0]->shape(channel_axis_);
num_output_ = this->layer_param_.convolution_param().num_output();
CHECK_GT(num_output_, 0);
group_ = this->layer_param_.convolution_param().group();
CHECK_EQ(channels_ % group_, 0);
CHECK_EQ(num_output_ % group_, 0)
<< "Number of output should be multiples of group.";
if (reverse_dimensions()) {
conv_out_channels_ = channels_;
conv_in_channels_ = num_output_;
} else {
conv_out_channels_ = num_output_;
conv_in_channels_ = channels_;
}
// Handle the parameters: weights and biases.
// - blobs_[0] holds the filter weights
// - blobs_[1] holds the biases (optional)
vector<int_tp> weight_shape(2);
weight_shape[0] = conv_out_channels_;
weight_shape[1] = conv_in_channels_ / group_;
for (int_tp i = 0; i < num_spatial_axes_; ++i) {
weight_shape.push_back(kernel_shape_data[i]);
}
bias_term_ = this->layer_param_.convolution_param().bias_term();
vector<int_tp> bias_shape(bias_term_, num_output_);
if (this->blobs_.size() > 0) {
CHECK_EQ(1 + bias_term_, this->blobs_.size())
<< "Incorrect number of weight blobs.";
if (weight_shape != this->blobs_[0]->shape()) {
Blob<Dtype> weight_shaped_blob(weight_shape);
LOG(FATAL) << "Incorrect weight shape: expected shape "
<< weight_shaped_blob.shape_string() << "; instead, shape was "
<< this->blobs_[0]->shape_string();
}
if (bias_term_ && bias_shape != this->blobs_[1]->shape()) {
Blob<Dtype> bias_shaped_blob(bias_shape);
LOG(FATAL) << "Incorrect bias shape: expected shape "
<< bias_shaped_blob.shape_string() << "; instead, shape was "
<< this->blobs_[1]->shape_string();
}
LOG(INFO) << "Skipping parameter initialization";
} else {
if (bias_term_) {
this->blobs_.resize(2);
} else {
this->blobs_.resize(1);
}
// Initialize and fill the weights:
// output channels x input channels per-group x kernel height x kernel width
this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
this->layer_param_.convolution_param().weight_filler()));
weight_filler->Fill(this->blobs_[0].get());
// If necessary, initialize and fill the biases.
if (bias_term_) {
this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
this->layer_param_.convolution_param().bias_filler()));
bias_filler->Fill(this->blobs_[1].get());
}
}
kernel_dim_ = this->blobs_[0]->count(1);
weight_offset_ = conv_out_channels_ * kernel_dim_ / group_;
// Propagate gradients to the parameters (as directed by backward pass).
this->param_propagate_down_.resize(this->blobs_.size(), true);
}
void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
if (is_direct_connect_ && !is_direct_intialized_)
{
direct_num_ = std::min((int)((direct_ratio_)*(num_output_ / (1 - direct_ratio_))), bottom[0]->channels());
this->blobs_.push_back(shared_ptr<Blob<Dtype> >());
vector<int> idx_shape;
idx_shape.push_back(direct_num_);
int idx_param_idx = this->blobs_.size() - 1;
this->blobs_[idx_param_idx].reset(new Blob<Dtype>(idx_shape));
vector<int> idx_tmp;
for (int i = 0; i < bottom[0]->channels(); i++)
idx_tmp.push_back(i);
std::random_shuffle(idx_tmp.begin(), idx_tmp.end());
for (int i = 0; i < direct_num_; i++)
//direct_idx_.push_back(idx_tmp[i]);
this->blobs_[idx_param_idx]->mutable_cpu_data()[i] = idx_tmp[i];
is_direct_intialized_ = true;
}
const int first_spatial_axis = channel_axis_ + 1;
CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_)
<< "bottom num_axes may not change.";
num_ = bottom[0]->count(0, channel_axis_);
CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
<< "Input size incompatible with convolution kernel.";
// TODO: generalize to handle inputs of different shapes.
for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
<< "All inputs must have the same shape.";
}
// Shape the tops.
bottom_shape_ = &bottom[0]->shape();
compute_output_shape();
vector<int> top_shape(bottom[0]->shape().begin(),
bottom[0]->shape().begin() + channel_axis_);
if (is_direct_connect_)
top_shape.push_back(num_output_ + direct_num_);
else
top_shape.push_back(num_output_);
for (int i = 0; i < num_spatial_axes_; ++i) {
top_shape.push_back(output_shape_[i]);
}
for (int top_id = 0; top_id < top.size(); ++top_id) {
top[top_id]->Reshape(top_shape);
}
if (reverse_dimensions()) {
conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
} else {
conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
}
col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
// Setup input dimensions (conv_input_shape_).
vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
conv_input_shape_.Reshape(bottom_dim_blob_shape);
int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
if (reverse_dimensions()) {
conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
} else {
conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
}
}
// The im2col result buffer will only hold one image at a time to avoid
// overly large memory usage. In the special case of 1x1 convolution
// it goes lazily unused to save memory.
col_buffer_shape_.clear();
col_buffer_shape_.push_back(kernel_dim_ * group_);
for (int i = 0; i < num_spatial_axes_; ++i) {
if (reverse_dimensions()) {
col_buffer_shape_.push_back(input_shape(i + 1));
} else {
col_buffer_shape_.push_back(output_shape_[i]);
}
}
col_buffer_.Reshape(col_buffer_shape_);
bottom_dim_ = bottom[0]->count(channel_axis_);
if (is_direct_connect_)
top_dim_ = top[0]->count(channel_axis_) * num_output_ / (direct_num_ + num_output_);
else
top_dim_ = top[0]->count(channel_axis_);
num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
// Set up the all ones "bias multiplier" for adding biases by BLAS
out_spatial_dim_ = top[0]->count(first_spatial_axis);
if (bias_term_) {
vector<int> bias_multiplier_shape(1, out_spatial_dim_);
bias_multiplier_.Reshape(bias_multiplier_shape);
caffe_set(bias_multiplier_.count(), Dtype(1),
bias_multiplier_.mutable_cpu_data());
}
}
