COMPMID-2336 Extend validation for depthwise native and fix same pad calculator
Change-Id: I9f5cc95bc0cbd94869ac13064ffa0aa0f52a7a61
Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1684
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h
index b0e2632..bc461e7 100644
--- a/arm_compute/core/Utils.h
+++ b/arm_compute/core/Utils.h
@@ -839,10 +839,12 @@
* @param[in] conv_info Convolution information (containing strides)
* @param[in] data_layout (Optional) Data layout of the input and weights tensor
* @param[in] dilation (Optional) Dilation factor used in the convolution.
+ * @param[in] rounding_type (Optional) Dimension rounding type when down-scaling.
*
* @return PadStrideInfo for SAME padding
*/
-PadStrideInfo calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout = DataLayout::NCHW, const Size2D &dilation = Size2D(1u, 1u));
+PadStrideInfo calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout = DataLayout::NCHW, const Size2D &dilation = Size2D(1u, 1u),
+ const DimensionRoundingType &rounding_type = DimensionRoundingType::FLOOR);
/** Returns expected width and height of the deconvolution's output tensor.
*
diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp
index 5d32750..d0bffdf 100644
--- a/src/core/Utils.cpp
+++ b/src/core/Utils.cpp
@@ -331,37 +331,44 @@
return res;
}
-PadStrideInfo arm_compute::calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout, const Size2D &dilation)
+PadStrideInfo arm_compute::calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout, const Size2D &dilation,
+ const DimensionRoundingType &rounding_type)
{
- const unsigned int width_idx = arm_compute::get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = arm_compute::get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const auto &strides = conv_info.stride();
+ const unsigned int width_idx = arm_compute::get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = arm_compute::get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int in_width = input_shape[width_idx];
+ const unsigned int in_height = input_shape[height_idx];
+ const unsigned int kernel_width = weights_shape[width_idx];
+ const unsigned int kernel_height = weights_shape[height_idx];
+ const auto &strides = conv_info.stride();
// Calculate output dimensions
- const int out_width = (input_shape[width_idx] + strides.first - 1) / strides.first;
- const int out_height = (input_shape[height_idx] + strides.second - 1) / strides.second;
+ const auto is_ceil = static_cast<unsigned int>(rounding_type == DimensionRoundingType::CEIL);
+ const unsigned int out_width = ((in_width - is_ceil) + strides.first - 1) / strides.first + is_ceil;
+ const unsigned int out_height = ((in_height - is_ceil) + strides.second - 1) / strides.second + is_ceil;
// Calculate effective weights sizes
- const int real_weight_width = (weights_shape[width_idx] - 1) * dilation.x() + 1;
- const int real_weight_height = (weights_shape[height_idx] - 1) * dilation.y() + 1;
+ const int real_weight_width = (kernel_width - 1) * dilation.x() + 1;
+ const int real_weight_height = (kernel_height - 1) * dilation.y() + 1;
// Calculate total pad
- const int pad_width = (out_width - 1) * strides.first + real_weight_width - input_shape[width_idx];
- const int pad_height = (out_height - 1) * strides.second + real_weight_height - input_shape[height_idx];
+ const int pad_width = std::max(0, static_cast<int>((out_width - 1) * strides.first + real_weight_width - in_width));
+ const int pad_height = std::max(0, static_cast<int>((out_height - 1) * strides.second + real_weight_height - in_height));
// Calculate individual paddings
- const int same_pad_left = pad_width / 2;
- const int same_pad_top = pad_height / 2;
- const int same_pad_right = pad_width - same_pad_left;
- const int same_pad_bottom = pad_height - same_pad_top;
+ const unsigned int pad_left = pad_width / 2;
+ const unsigned int pad_top = pad_height / 2;
+ const unsigned int pad_right = pad_width - pad_left;
+ const unsigned int pad_bottom = pad_height - pad_top;
- return { static_cast<unsigned int>(strides.first),
- static_cast<unsigned int>(strides.second),
- static_cast<unsigned int>(same_pad_left),
- static_cast<unsigned int>(same_pad_right),
- static_cast<unsigned int>(same_pad_top),
- static_cast<unsigned int>(same_pad_bottom),
- DimensionRoundingType::CEIL };
+ PadStrideInfo same_info(strides.first, strides.second, pad_left, pad_right, pad_top, pad_bottom, rounding_type);
+
+ // Check for correctness of predicted output shape against the one calculated using the generated info
+ const auto out_dims = scaled_dimensions(in_width, in_height, kernel_width, kernel_height, same_info, dilation);
+ ARM_COMPUTE_ERROR_ON(out_dims.first != out_width || out_dims.second != out_height);
+ ARM_COMPUTE_UNUSED(out_dims);
+
+ return same_info;
}
std::pair<unsigned int, unsigned int> arm_compute::deconvolution_output_dimensions(
diff --git a/tests/validation/NEON/DepthwiseConvolutionNativeLayer.cpp b/tests/validation/NEON/DepthwiseConvolutionNativeLayer.cpp
index a44c2dc..64f6a93 100644
--- a/tests/validation/NEON/DepthwiseConvolutionNativeLayer.cpp
+++ b/tests/validation/NEON/DepthwiseConvolutionNativeLayer.cpp
@@ -75,8 +75,11 @@
/** Batch values to test - Nightly */
const auto batch_values_nightly = framework::dataset::make("batch", { 1U, 3U });
-/** Kernel size values to test - All */
-const auto kernel_sz_values = framework::dataset::make("kernel_size", { Size2D(3U, 5U), Size2D(5U, 3U) });
+/** Kernel size values to test - Precommit */
+const auto kernel_sz_values_precommit = framework::dataset::make("kernel_size", { Size2D(1U, 1U), Size2D(1U, 3U) });
+
+/** Kernel size values to test - Nightly */
+const auto kernel_sz_values_nightly = framework::dataset::make("kernel_size", { Size2D(3U, 5U), Size2D(5U, 1U), Size2D(1U, 7U), Size2D(9U, 7U) });
/** Depth multiplier values to test - All */
const auto depth_multiplier_values = framework::dataset::make("depth_multiplier", { 1U, 3U });
@@ -154,7 +157,7 @@
height_values_precommit),
channel_values_precommit),
batch_values_precommit),
- kernel_sz_values),
+ kernel_sz_values_precommit),
depth_multiplier_values),
dilation_values),
stride_values),
@@ -171,7 +174,7 @@
height_values_precommit),
channel_values_precommit),
batch_values_precommit),
- kernel_sz_values),
+ kernel_sz_values_precommit),
depth_multiplier_values),
dilation_values),
stride_values),
@@ -188,7 +191,7 @@
height_values_nightly),
channel_values_nightly),
batch_values_nightly),
- kernel_sz_values),
+ kernel_sz_values_nightly),
depth_multiplier_values),
dilation_values),
stride_values),