COMPMID-617 Add validation methods to Kernels
- NEActivationLayer
- NESoftmax
- NEDirectConvolutionLayer
- NENormalizationLayer
- NEPoolingLayer
Change-Id: Ib279f1c1b7f9247679b0d6593aed7393da8fe87b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111335
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
index 0e06704..47372c2 100644
--- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
@@ -47,6 +47,15 @@
namespace
{
+void auto_init(const ITensorInfo *input, ITensorInfo *output, unsigned int pooled_w, unsigned int pooled_h)
+{
+ TensorShape output_shape{ input->tensor_shape() };
+ output_shape.set(0, pooled_w);
+ output_shape.set(1, pooled_h);
+
+ auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
+}
+
template <bool exclude_padding>
inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h,
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
@@ -88,75 +97,77 @@
const int val = ((end_y - start_y) * (end_x - start_x));
return sshr_qs16(scale_values_q16[val], (15 - fixed_point_position));
}
-} // namespace
-NEPoolingLayerKernel::NEPoolingLayerKernel()
- : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &pooled_w, unsigned int pooled_h, int pool_size)
{
-}
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-BorderSize NEPoolingLayerKernel::border_size() const
-{
- return _border_size;
-}
-
-void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info)
-{
int pool_pad_x = 0;
int pool_pad_y = 0;
int pool_stride_x = 0;
int pool_stride_y = 0;
- unsigned int pooled_w = 0;
- unsigned int pooled_h = 0;
PoolingType pool_type = pool_info.pool_type();
- int pool_size = pool_info.pool_size();
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
const bool exclude_padding = pool_info.exclude_padding();
const bool is_global_pooling = pool_info.is_global_pooling();
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
-
static const std::set<int> supported_pool_sizes = { 2, 3 };
- ARM_COMPUTE_UNUSED(supported_pool_sizes);
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_fixed_point(input->info()->data_type()));
- ARM_COMPUTE_ERROR_ON((supported_pool_sizes.find(pool_size) == supported_pool_sizes.end()) && (input->info()->data_type() != DataType::F32));
- ARM_COMPUTE_ERROR_ON(!is_global_pooling && (pool_pad_x >= pool_size || pool_pad_y >= pool_size));
- ARM_COMPUTE_ERROR_ON(is_global_pooling && (input->info()->tensor_shape().x() != input->info()->tensor_shape().y()));
- ARM_COMPUTE_ERROR_ON(is_data_type_fixed_point(input->info()->data_type()) && pool_stride_x > 2);
- ARM_COMPUTE_ERROR_ON(exclude_padding && is_data_type_fixed_point(input->info()->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_fixed_point(input->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON((supported_pool_sizes.find(pool_size) == supported_pool_sizes.end()) && (input->data_type() != DataType::F32));
+ ARM_COMPUTE_RETURN_ERROR_ON(!is_global_pooling && (pool_pad_x >= pool_size || pool_pad_y >= pool_size));
+ ARM_COMPUTE_RETURN_ERROR_ON(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()));
+ ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_fixed_point(input->data_type()) && pool_stride_x > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(exclude_padding && is_data_type_fixed_point(input->data_type()));
- // Update pool size in case of global pooling
- pool_size = is_global_pooling ? input->info()->dimension(0) : pool_size;
-
- // Check output dimensions
- std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
- input->info()->dimension(1),
- pool_size,
- pool_size,
- pool_info.pad_stride_info());
-
- // Output auto initialization if not yet initialized
+ if(output->total_size() != 0)
{
- TensorShape output_shape{ input->info()->tensor_shape() };
- output_shape.set(0, pooled_w);
- output_shape.set(1, pooled_h);
-
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(0) != pooled_w) || (output->dimension(1) != pooled_h));
}
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
- ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));
+ return Status{};
+}
- unsigned int num_elems_read_per_iteration = 0;
- unsigned int num_elems_processed_per_iteration = 0;
- unsigned int num_elems_horizontal_window = 0;
+Status validate_arguments_pool_info(const ITensorInfo *input, const PoolingLayerInfo &pool_info, const unsigned int pool_size)
+{
+ const bool is_global_pooling = pool_info.is_global_pooling();
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()),
+ "Global pooling is supported only with rectangular inputs!");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)),
+ "Invalid pool size and pool pad combination!");
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &num_elems_processed_per_iteration,
+ BorderSize &border_size,
+ unsigned int pooled_w, unsigned int pooled_h, int pool_size)
+{
+ unsigned int num_elems_read_per_iteration = 0;
+ unsigned int num_elems_horizontal_window = 0;
+ int pool_pad_x = 0;
+ int pool_pad_y = 0;
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ const int input_width = input->dimension(0);
+ const int input_height = input->dimension(1);
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
+ std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+ std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
+
+ // Check output dimensions
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0),
+ input->dimension(1),
+ pool_size,
+ pool_size,
+ pad_stride_info);
// Select element size
- switch(input->info()->data_type())
+ switch(input->data_type())
{
case DataType::QS8:
num_elems_read_per_iteration = 16;
@@ -233,19 +244,89 @@
break;
}
- _num_elems_processed_per_iteration = num_elems_processed_per_iteration;
- const int input_width = input->info()->dimension(0);
- const int input_height = input->info()->dimension(1);
- const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
- const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+ const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
+ const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+ border_size = BorderSize(pool_pad_y, pool_pad_x);
+ border_size.right = std::max(upper_bound_w, pool_pad_x);
+ border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+ bool window_changed = false;
+
+ TensorShape output_shape{ input->tensor_shape() };
+ output_shape.set(0, pooled_w);
+ output_shape.set(1, pooled_h);
+ TensorInfo output_info(input->clone()->set_tensor_shape(output_shape));
+
+ Window win = calculate_max_window(output_info, Steps(num_elems_processed_per_iteration));
+ AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + border_size.right, input_height + border_size.bottom);
+
+ if(output->total_size() != 0)
+ {
+ AccessWindowHorizontal output_access(output, 0, num_elems_horizontal_window);
+ window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ }
+ else
+ {
+ window_changed = update_window_and_padding(win, input_access);
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
+NEPoolingLayerKernel::NEPoolingLayerKernel()
+ : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0)
+{
+}
+
+BorderSize NEPoolingLayerKernel::border_size() const
+{
+ return _border_size;
+}
+
+void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+ int pool_pad_x = 0;
+ int pool_pad_y = 0;
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ unsigned int pooled_w = 0;
+ unsigned int pooled_h = 0;
+ PoolingType pool_type = pool_info.pool_type();
+ int pool_size = pool_info.pool_size();
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
+ const bool exclude_padding = pool_info.exclude_padding();
+ const bool is_global_pooling = pool_info.is_global_pooling();
+ std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
+ std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+
+ // Update pool size in case of global pooling
+ pool_size = is_global_pooling ? input->info()->dimension(0) : pool_size;
+
+ // Validate pool info before calling scaled_dimensions
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_pool_info(input->info(), pool_info, pool_size));
+
+ // Check output dimensions
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
+ input->info()->dimension(1),
+ pool_size,
+ pool_size,
+ pool_info.pad_stride_info());
+
+ // Output auto initialization if not yet initialized
+ auto_init(input->info(), output->info(), pooled_w, pooled_h);
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h, pool_size));
// Set instance variables
- _input = input;
- _output = output;
- _pool_info = pool_info;
- _border_size = BorderSize(pool_pad_y, pool_pad_x);
- _border_size.right = std::max(upper_bound_w, pool_pad_x);
- _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+ _input = input;
+ _output = output;
+ _pool_info = pool_info;
// Select appropriate function
switch(pool_size)
@@ -413,12 +494,9 @@
}
// Configure kernel window
- Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
- AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window);
- update_window_and_padding(win, input_access, output_access);
- output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
- INEKernel::configure(win);
+ auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info, _num_elems_processed_per_iteration, _border_size, pooled_w, pooled_h, pool_size);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ INEKernel::configure(win_config.second);
}
template <PoolingType pooling_type>
@@ -1154,6 +1232,34 @@
input, output);
}
+Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+
+ unsigned int pooled_w = 0;
+ unsigned int pooled_h = 0;
+ unsigned int num_elems_processed_per_iteration = 0;
+ BorderSize border_size(0);
+
+ const bool is_global_pooling = pool_info.is_global_pooling();
+ const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size();
+
+ // Validate pool info befor calling scaled_dimensions
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_pool_info(input, pool_info, pool_size));
+
+ // Check output dimensions
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0),
+ input->dimension(1),
+ pool_size,
+ pool_size,
+ pool_info.pad_stride_info());
+
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h, pool_size));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, pool_size).first);
+
+ return Status{};
+}
+
void NEPoolingLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);