COMPMID-1710: Add explicit padding arguments to depthwise convolution
Change-Id: I3011640f4d4d80b7f4e488ec8df47454d3220c5d
Reviewed-on: https://review.mlplatform.org/484
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
diff --git a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp
index e262817..b33f276 100644
--- a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -41,6 +41,24 @@
const unsigned int CHANNEL_BLOCK = 16;
+namespace
+{
+ inline int pad_along_dim(
+ const bool padding_same,
+ const int kernel_dim,
+ const int stride_dim,
+ const int input_dim
+ )
+ {
+ if (!padding_same)
+ return 0;
+ if (input_dim % stride_dim)
+ return std::max(kernel_dim - (input_dim % stride_dim), 0);
+ else
+ return std::max(kernel_dim - stride_dim, 0);
+ }
+} // namespace
+
template <int OTR, int OTC, int KR, int KC, int SR, int SC, typename TIn, typename TOut>
int DepthwiseConvolution<OTR, OTC, KR, KC, SR, SC, TIn, TOut>::get_output_size(
const int dim_size, const bool same_padding
@@ -49,6 +67,13 @@
return iceildiv(dim_size - (same_padding ? 0 : (KC - 1)), SR);
}
+template <int OTR, int OTC, int KR, int KC, int SR, int SC, typename TIn, typename TOut>
+int DepthwiseConvolution<OTR, OTC, KR, KC, SR, SC, TIn, TOut>::get_output_size(
+ const int dim_size, const unsigned int padding_before, const unsigned int padding_after
+)
+{
+ return iceildiv(dim_size + padding_before + padding_after - KR + 1, SR);
+}
template <int OTR, int OTC, int KR, int KC, int SR, int SC, typename TIn, typename TOut>
DepthwiseConvolution<OTR, OTC, KR, KC, SR, SC, TIn, TOut>::DepthwiseConvolution(
@@ -65,16 +90,54 @@
const int output_col_stride,
const int output_row_stride,
const int output_batch_stride
+) : DepthwiseConvolution<OTR, OTC, KR, KC, SR, SC, TIn, TOut>(
+ n_batches, n_input_rows, n_input_cols,
+ n_channels,
+ pad_along_dim(padding_same, KR, SR, n_input_rows) / 2, /* top padding */
+ pad_along_dim(padding_same, KC, SC, n_input_cols) / 2, /* left padding */
+ iceildiv(pad_along_dim(padding_same, KR, SR, n_input_rows), 2), /* bottom padding */
+ iceildiv(pad_along_dim(padding_same, KC, SC, n_input_cols), 2), /* right padding */
+ weights, input, output,
+ weight_col_stride, weight_row_stride,
+ input_col_stride, input_row_stride, input_batch_stride,
+ output_col_stride, output_row_stride, output_batch_stride
+)
+{
+}
+
+
+template <int OTR, int OTC, int KR, int KC, int SR, int SC, typename TIn, typename TOut>
+DepthwiseConvolution<OTR, OTC, KR, KC, SR, SC, TIn, TOut>::DepthwiseConvolution(
+ const int n_batches, const int n_input_rows, const int n_input_cols,
+ const int n_channels,
+ const unsigned int padding_top,
+ const unsigned int padding_left,
+ const unsigned int padding_bottom,
+ const unsigned int padding_right,
+ const TIn* const weights,
+ const TIn* const input,
+ TOut* const output,
+ const int weight_col_stride,
+ const int weight_row_stride,
+ const int input_col_stride,
+ const int input_row_stride,
+ const int input_batch_stride,
+ const int output_col_stride,
+ const int output_row_stride,
+ const int output_batch_stride
) : _weights(weights), _input(input), _output(output),
_n_batches(n_batches),
_n_input_rows(n_input_rows),
_n_input_cols(n_input_cols),
_n_channels(n_channels),
- _n_output_rows(get_output_size(n_input_rows, padding_same)),
- _n_output_cols(get_output_size(n_input_cols, padding_same)),
+ _n_output_rows(get_output_size(n_input_rows, padding_top, padding_bottom)),
+ _n_output_cols(get_output_size(n_input_cols, padding_left, padding_right)),
_n_tile_rows(iceildiv(_n_output_rows, output_tile_rows)),
_n_tile_cols(iceildiv(_n_output_cols, output_tile_cols)),
- _padding_same(padding_same),
+ _padding_top(padding_top),
+ _padding_left(padding_left),
+ _padding_bottom(padding_bottom),
+ _padding_right(padding_right),
_weight_col_stride(weight_col_stride ? weight_col_stride : _n_channels),
_weight_row_stride(weight_row_stride ? weight_row_stride : KC * _weight_col_stride),
_input_col_stride(input_col_stride ? input_col_stride : _n_channels),
@@ -113,10 +176,8 @@
const auto stop_channel = std::min<unsigned int>(_n_channels, CHANNEL_BLOCK * stop);
// Compute top and bottom padding for input and output
- const int input_pad_top = _padding_same ?
- ((_n_output_rows - 1)*stride_rows + kernel_rows - _n_input_rows) / 2 : 0;
- const int input_pad_left = _padding_same ?
- ((_n_output_cols - 1)*stride_cols + kernel_cols - _n_input_cols) / 2 : 0;
+ const int input_pad_top = _padding_top;
+ const int input_pad_left = _padding_left;
constexpr int tile_overlap = kernel_rows - stride_rows;
// Perform the convolution by calling `process_tile_row` for each tile row in