COMPMID-692 Consistent names for the interfaces

Change-Id: I4b1f3f0da9ff5342c7de7083736fe91871d14e5b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110351
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>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 126 insertions, 0 deletions

diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
new file mode 100644
index 0000000000..b890c6f5d5
--- /dev/null
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3()
+    : _kernel(), _bias_kernel(), _border_handler(), _has_bias(false)
+{
+}
+
+void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
+
+    // Call convolution kernel
+    _kernel.configure(input, weights, output, conv_info);
+    _border_handler.configure(input, _kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast<float>(0.f)));
+    if(biases != nullptr)
+    {
+        _bias_kernel.configure(output, biases);
+        _has_bias = true;
+    }
+}
+
+void NEDepthwiseConvolutionLayer3x3::run()
+{
+    NEScheduler::get().schedule(&_border_handler, Window::DimX);
+    NEScheduler::get().schedule(&_kernel, Window::DimX);
+    if(_has_bias)
+    {
+        NEScheduler::get().schedule(&_bias_kernel, Window::DimX);
+    }
+}
+
+NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer()
+    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _input_reshaped(), _weights_reshaped(), _v2mm_output()
+{
+}
+
+void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != weights->info()->dimension(2));
+
+    const size_t weights_w = weights->info()->dimension(0);
+    const size_t weights_h = weights->info()->dimension(1);
+    const size_t weights_z = weights->info()->dimension(2);
+
+    bool has_bias = (biases != nullptr);
+
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights_w, weights_h, conv_info);
+
+    // Set up intermediate tensors
+    const size_t patch_size = weights_w * weights_h + ((has_bias) ? 1 : 0);
+    const size_t conv_size  = conv_w * conv_h;
+
+    // Im2Col configuration
+    TensorShape shape_im2col = input->info()->tensor_shape();
+    shape_im2col.set(0, patch_size);
+    shape_im2col.set(1, conv_size);
+    shape_im2col.set(2, weights_z);
+    const TensorInfo info_im2col(shape_im2col, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _input_reshaped.allocator()->init(info_im2col);
+    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, has_bias);
+
+    // Weights reshape configuration
+    const TensorShape shape_weights_reshape(patch_size, weights_z);
+    const TensorInfo  info_weights_reshape(shape_weights_reshape, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
+    _weights_reshaped.allocator()->init(info_weights_reshape);
+    _weights_reshape_kernel.configure(weights, &_weights_reshaped, biases);
+
+    // GEMV configuration
+    TensorShape shape_v2mm_out = input->info()->tensor_shape();
+    shape_v2mm_out.set(0, conv_size * weights_z);
+    shape_v2mm_out.set(1, 1);
+    shape_v2mm_out.set(2, 1);
+    const TensorInfo info_v2mm_out(shape_v2mm_out, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _v2mm_output.allocator()->init(info_v2mm_out);
+    _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
+    _vector_to_tensor_kernel.configure(&_v2mm_output, output, conv_w, conv_h);
+
+    // Allocate intermediate tensors
+    _input_reshaped.allocator()->allocate();
+    _weights_reshaped.allocator()->allocate();
+    _v2mm_output.allocator()->allocate();
+}
+
+void NEDepthwiseConvolutionLayer::run()
+{
+    NEScheduler::get().schedule(&_im2col_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_weights_reshape_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX);
+    NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX);
+}
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