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Upsampling layer fixed for 0 dimension
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@milakov
milakov committed Feb 21, 2016
1 parent 4e522a1 commit 59be84f
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Showing 7 changed files with 82 additions and 27 deletions.
22 changes: 16 additions & 6 deletions nnforge/cuda/upsampling_layer_tester_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -187,22 +187,32 @@ namespace nnforge
feature_map_upsampling_size = layer_derived->feature_map_upsampling_size;
entry_upsampling_size = layer_derived->entry_upsampling_size;

int_fastdiv current_stride(layer_derived->upsampling_sizes[0]);
std::vector<unsigned int> local_upsampling_sizes = layer_derived->upsampling_sizes;
if (local_upsampling_sizes.empty())
local_upsampling_sizes.push_back(1);
std::vector<unsigned int> local_input_dimension_sizes = input_configuration_specific_list[0].dimension_sizes;
if (local_input_dimension_sizes.empty())
local_input_dimension_sizes.push_back(1);
std::vector<unsigned int> local_output_dimension_sizes = output_configuration_specific.dimension_sizes;
if (local_output_dimension_sizes.empty())
local_output_dimension_sizes.push_back(1);

int_fastdiv current_stride(local_upsampling_sizes[0]);
for(int i = 0; i < dimension_count; ++i)
{
upsampling_sizes[i] = layer_derived->upsampling_sizes[i];
input_sizes[i] = input_configuration_specific_list[0].dimension_sizes[i];
output_sizes[i] = output_configuration_specific.dimension_sizes[i];
upsampling_sizes[i] = local_upsampling_sizes[i];
input_sizes[i] = local_input_dimension_sizes[i];
output_sizes[i] = local_output_dimension_sizes[i];
strides[i] = current_stride;

current_stride = current_stride * static_cast<int>(input_configuration_specific_list[0].dimension_sizes[i]);
current_stride = current_stride * input_sizes[i];
}

forward_packed_config_count = upsampling_sizes[0];
for(int i = 0; i < dimension_count; ++i)
forward_packed_config_count *= input_sizes[i];

nonunit_window_x = (layer_derived->upsampling_sizes[0] > 1);
nonunit_window_x = (local_upsampling_sizes[0] > 1);
}

private:
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5 changes: 4 additions & 1 deletion nnforge/cuda/upsampling_layer_testing_schema.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,10 @@ namespace nnforge

switch (output_configuration_specific.dimension_sizes.size())
{
case 1:
case 0:
res = layer_tester_cuda::ptr(new upsampling_layer_tester_cuda<1>());
break;
case 1:
res = layer_tester_cuda::ptr(new upsampling_layer_tester_cuda<1>());
break;
case 2:
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22 changes: 16 additions & 6 deletions nnforge/cuda/upsampling_layer_updater_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -400,22 +400,32 @@ namespace nnforge
feature_map_upsampling_size = layer_derived->feature_map_upsampling_size;
entry_upsampling_size = layer_derived->entry_upsampling_size;

int_fastdiv current_stride(layer_derived->upsampling_sizes[0]);
std::vector<unsigned int> local_upsampling_sizes = layer_derived->upsampling_sizes;
if (local_upsampling_sizes.empty())
local_upsampling_sizes.push_back(1);
std::vector<unsigned int> local_input_dimension_sizes = input_configuration_specific_list[0].dimension_sizes;
if (local_input_dimension_sizes.empty())
local_input_dimension_sizes.push_back(1);
std::vector<unsigned int> local_output_dimension_sizes = output_configuration_specific.dimension_sizes;
if (local_output_dimension_sizes.empty())
local_output_dimension_sizes.push_back(1);

int_fastdiv current_stride(local_upsampling_sizes[0]);
for(int i = 0; i < dimension_count; ++i)
{
upsampling_sizes[i] = layer_derived->upsampling_sizes[i];
input_sizes[i] = input_configuration_specific_list[0].dimension_sizes[i];
output_sizes[i] = output_configuration_specific.dimension_sizes[i];
upsampling_sizes[i] = local_upsampling_sizes[i];
input_sizes[i] = local_input_dimension_sizes[i];
output_sizes[i] = local_output_dimension_sizes[i];
strides[i] = current_stride;

current_stride = current_stride * static_cast<int>(input_configuration_specific_list[0].dimension_sizes[i]);
current_stride = current_stride * static_cast<int>(local_input_dimension_sizes[i]);
}

forward_packed_config_count = upsampling_sizes[0];
for(int i = 0; i < dimension_count; ++i)
forward_packed_config_count *= input_sizes[i];

nonunit_window_x = (layer_derived->upsampling_sizes[0] > 1);
nonunit_window_x = (local_upsampling_sizes[0] > 1);
}

bool is_backward_data_dependent_on_input_buffer(unsigned int action_input_index, unsigned int data_input_index) const
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3 changes: 3 additions & 0 deletions nnforge/cuda/upsampling_layer_updater_schema.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,9 @@ namespace nnforge

switch (output_configuration_specific.dimension_sizes.size())
{
case 0:
res = layer_updater_cuda::ptr(new upsampling_layer_updater_cuda<1>());
break;
case 1:
res = layer_updater_cuda::ptr(new upsampling_layer_updater_cuda<1>());
break;
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14 changes: 11 additions & 3 deletions nnforge/plain/upsampling_layer_tester_plain.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,12 @@ namespace nnforge
const layer_configuration_specific& output_configuration_specific,
unsigned int entry_count) const
{
std::vector<unsigned int> input_dimension_sizes = input_configuration_specific_list[0].dimension_sizes;
if (input_dimension_sizes.empty())
input_dimension_sizes.push_back(1);
std::vector<unsigned int> output_dimension_sizes = output_configuration_specific.dimension_sizes;
if (output_dimension_sizes.empty())
output_dimension_sizes.push_back(1);
const float * const in_it_global = *input_buffers[0];
float * const out_it_global = *output_buffer;
const unsigned int input_neuron_count = input_configuration_specific_list[0].get_neuron_count();
Expand All @@ -61,17 +67,19 @@ namespace nnforge
const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
nnforge_shared_ptr<const upsampling_layer> layer_derived = nnforge_dynamic_pointer_cast<const upsampling_layer>(layer_schema);
std::vector<unsigned int> upsampling_sizes = layer_derived->upsampling_sizes;
if (upsampling_sizes.empty())
upsampling_sizes.push_back(1);
const unsigned int feature_map_upsampling_size = layer_derived->feature_map_upsampling_size;
upsampling_sizes.push_back(feature_map_upsampling_size);
const unsigned int entry_upsampling_size = layer_derived->entry_upsampling_size;
upsampling_sizes.push_back(entry_upsampling_size);
const unsigned int upsampling_dimension_count = static_cast<unsigned int>(upsampling_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_configuration_specific.dimension_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_dimension_sizes.size());
std::vector<unsigned int> output_slices(upsampling_sizes.size());
output_slices[0] = 1;
for(unsigned int i = 0; i < upsampling_dimension_count - 1; ++i)
{
int dimension_size = (i < spatial_dimension_count) ? output_configuration_specific.dimension_sizes[i] : output_configuration_specific.feature_map_count;
int dimension_size = (i < spatial_dimension_count) ? output_dimension_sizes[i] : output_configuration_specific.feature_map_count;
output_slices[i + 1] = output_slices[i] * dimension_size;
}
unsigned int upsampling_elem_count = 1;
Expand Down Expand Up @@ -99,7 +107,7 @@ namespace nnforge
}

const int total_workload = (entry_count / entry_upsampling_size) * input_configuration_specific_list[0].feature_map_count;
const std::vector<unsigned int>::const_iterator dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator dimension_sizes_it = input_dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator upsampling_sizes_it = upsampling_sizes.begin();
const std::vector<unsigned int>::const_iterator output_slices_it = output_slices.begin();
const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();
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28 changes: 22 additions & 6 deletions nnforge/plain/upsampling_layer_updater_plain.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,12 @@ namespace nnforge
const std::set<layer_action>& actions,
unsigned int entry_count) const
{
std::vector<unsigned int> input_dimension_sizes = input_configuration_specific_list[0].dimension_sizes;
if (input_dimension_sizes.empty())
input_dimension_sizes.push_back(1);
std::vector<unsigned int> output_dimension_sizes = output_configuration_specific.dimension_sizes;
if (output_dimension_sizes.empty())
output_dimension_sizes.push_back(1);
const float * const in_it_global = *input_buffers[0];
float * const out_it_global = *output_buffer;
const unsigned int input_neuron_count = input_configuration_specific_list[0].get_neuron_count();
Expand All @@ -63,17 +69,19 @@ namespace nnforge
const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
nnforge_shared_ptr<const upsampling_layer> layer_derived = nnforge_dynamic_pointer_cast<const upsampling_layer>(layer_schema);
std::vector<unsigned int> upsampling_sizes = layer_derived->upsampling_sizes;
if (upsampling_sizes.empty())
upsampling_sizes.push_back(1);
const unsigned int feature_map_upsampling_size = layer_derived->feature_map_upsampling_size;
upsampling_sizes.push_back(feature_map_upsampling_size);
const unsigned int entry_upsampling_size = layer_derived->entry_upsampling_size;
upsampling_sizes.push_back(entry_upsampling_size);
const unsigned int upsampling_dimension_count = static_cast<unsigned int>(upsampling_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_configuration_specific.dimension_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_dimension_sizes.size());
std::vector<unsigned int> output_slices(upsampling_sizes.size());
output_slices[0] = 1;
for(unsigned int i = 0; i < upsampling_dimension_count - 1; ++i)
{
int dimension_size = (i < spatial_dimension_count) ? output_configuration_specific.dimension_sizes[i] : output_configuration_specific.feature_map_count;
int dimension_size = (i < spatial_dimension_count) ? output_dimension_sizes[i] : output_configuration_specific.feature_map_count;
output_slices[i + 1] = output_slices[i] * dimension_size;
}
unsigned int upsampling_elem_count = 1;
Expand Down Expand Up @@ -101,7 +109,7 @@ namespace nnforge
}

const int total_workload = (entry_count / entry_upsampling_size) * input_configuration_specific_list[0].feature_map_count;
const std::vector<unsigned int>::const_iterator dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator dimension_sizes_it = input_dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator upsampling_sizes_it = upsampling_sizes.begin();
const std::vector<unsigned int>::const_iterator output_slices_it = output_slices.begin();
const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();
Expand Down Expand Up @@ -163,6 +171,12 @@ namespace nnforge
const std::set<layer_action>& actions,
unsigned int entry_count) const
{
std::vector<unsigned int> input_dimension_sizes = input_configuration_specific_list[0].dimension_sizes;
if (input_dimension_sizes.empty())
input_dimension_sizes.push_back(1);
std::vector<unsigned int> output_dimension_sizes = output_configuration_specific.dimension_sizes;
if (output_dimension_sizes.empty())
output_dimension_sizes.push_back(1);
float * const in_err_it_global = *input_errors_buffer;
const float * const out_err_it_global = *output_errors_buffer;
const unsigned int input_neuron_count = input_configuration_specific_list[0].get_neuron_count();
Expand All @@ -174,14 +188,16 @@ namespace nnforge
const unsigned int feature_map_upsampling_size = layer_derived->feature_map_upsampling_size;
upsampling_sizes.push_back(feature_map_upsampling_size);
const unsigned int entry_upsampling_size = layer_derived->entry_upsampling_size;
if (upsampling_sizes.empty())
upsampling_sizes.push_back(1);
upsampling_sizes.push_back(entry_upsampling_size);
const unsigned int upsampling_dimension_count = static_cast<unsigned int>(upsampling_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_configuration_specific.dimension_sizes.size());
const unsigned int spatial_dimension_count = static_cast<unsigned int>(output_dimension_sizes.size());
std::vector<unsigned int> output_slices(upsampling_sizes.size());
output_slices[0] = 1;
for(unsigned int i = 0; i < upsampling_dimension_count - 1; ++i)
{
int dimension_size = (i < spatial_dimension_count) ? output_configuration_specific.dimension_sizes[i] : output_configuration_specific.feature_map_count;
int dimension_size = (i < spatial_dimension_count) ? output_dimension_sizes[i] : output_configuration_specific.feature_map_count;
output_slices[i + 1] = output_slices[i] * dimension_size;
}
unsigned int upsampling_elem_count = 1;
Expand Down Expand Up @@ -209,7 +225,7 @@ namespace nnforge
}

const int total_workload = (entry_count / entry_upsampling_size) * input_configuration_specific_list[0].feature_map_count;
const std::vector<unsigned int>::const_iterator dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator dimension_sizes_it = input_dimension_sizes.begin();
const std::vector<unsigned int>::const_iterator upsampling_sizes_it = upsampling_sizes.begin();
const std::vector<unsigned int>::const_iterator output_slices_it = output_slices.begin();
const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();
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15 changes: 10 additions & 5 deletions nnforge/upsampling_layer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -41,9 +41,6 @@ namespace nnforge

void upsampling_layer::check()
{
if (upsampling_sizes.size() == 0)
throw neural_network_exception("upsampling sizes for average subsampling layer may not be empty");

for(unsigned int i = 0; i < upsampling_sizes.size(); i++)
{
if (upsampling_sizes[i] == 0)
Expand Down Expand Up @@ -204,10 +201,18 @@ namespace nnforge
}

if (feature_map_upsampling_size != 1)
ss << ", fm " << feature_map_upsampling_size;
{
if (!ss.str().empty())
ss << ", ";
ss << "fm " << feature_map_upsampling_size;
}

if (entry_upsampling_size != 1)
ss << ", samples " << entry_upsampling_size;
{
if (!ss.str().empty())
ss << ", ";
ss << "samples " << entry_upsampling_size;
}

res.push_back(ss.str());

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