--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__
+#define __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** OpenCL kernel to perform depthTospace operation */
+class CLDepthToSpaceKernel : public ICLKernel
+{
+public:
+ /** Default constructor */
+ CLDepthToSpaceKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLDepthToSpaceKernel(const CLDepthToSpaceKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLDepthToSpaceKernel &operator=(const CLDepthToSpaceKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ CLDepthToSpaceKernel(CLDepthToSpaceKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ CLDepthToSpaceKernel &operator=(CLDepthToSpaceKernel &&) = default;
+ /** Default destructor */
+ ~CLDepthToSpaceKernel() = default;
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32.
+ * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32.
+ */
+ void configure(const ICLTensor *input, ICLTensor *output, const int32_t block_size);
+
+ // Inherited methods overridden:
+ void run(const Window &window, cl::CommandQueue &queue) override;
+
+private:
+ const ICLTensor *_input; /**< Source tensor */
+ ICLTensor *_output; /**< Destination tensor */
+};
+
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__ */
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef __ARM_COMPUTE_CLDEPTHTOSPACE_H__
+#define __ARM_COMPUTE_CLDEPTHTOSPACE_H__
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to run @ref CLDepthToSpaceKernel
+ *
+ * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32.
+ * @note The function converts the input tensor to the tensor of the output tensor's type.
+ */
+class CLDepthToSpace : public ICLSimpleFunction
+{
+public:
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32.
+ * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32.
+ * @param[block_size] block size integer only
+ */
+ void configure(ICLTensor *input, ICLTensor *output, const int32_t block_size);
+};
+} // namesace arm_compute
+
+#endif /* __ARM_COMPUTE_CLDEPTHTOSPACE_H__ */
{"depthwise_im2col", "depthwise_convolution.cl"},
{"depthwise_vector_to_tensor", "depthwise_convolution.cl"},
{"depthwise_weights_reshape", "depthwise_convolution.cl"},
+ {"depth_to_space", "depth_to_space.cl"},
{"dequantization_layer", "dequantization_layer.cl"},
{"derivative", "derivative.cl"},
{"dilate", "dilate.cl"},
#include "./cl_kernels/equal_quantized.clembed"
},
{
+ "depth_to_space.cl",
+#include "./cl_kernels/depth_to_space.clembed"
+ },
+ {
"exp.cl",
#include "./cl_kernels/exp.clembed"
},
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "helpers.h"
+
+#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BLOCK_SIZE)
+/** Perform space to depth rearrangement of tensor
+ *
+ * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
+ * @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16
+ * @attention block size should be given as a preprocessor argument using -DBLOCK_SIZE=size. e.g. -DBLOCK_SIZE=1
+ *
+ * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p inpu
+t_ptr
+ * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
+ * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes)
+ * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in
+bytes)
+ * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image
+ */
+__kernel void depth_to_space(
+ TENSOR4D_DECLARATION(input),
+ TENSOR4D_DECLARATION(output))
+ {
+ Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0);
+ Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT);
+
+ int out_index[4]={0};
+ int in_index[4]={0};
+
+ out_index[0] = get_global_id(0);//W
+ out_index[1] = get_global_id(1);//H
+ out_index[2] = get_global_id(2) % DEPTH_OUT;//C
+ out_index[3] = get_global_id(2) / DEPTH_OUT;//B
+
+ in_index[0] = out_index[0]/BLOCK_SIZE;
+ in_index[1] = out_index[1]/BLOCK_SIZE;
+ in_index[2] = out_index[2] + ((out_index[1] % BLOCK_SIZE) * BLOCK_SIZE + out_index[0] % BLOCK_SIZE) * DEPTH_OUT;
+ in_index[3] = out_index[3];
+
+ *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], in_index[2],in_index[3]));
+ }
+#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BLOCK_SIZE)
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLKernelLibraryEx.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <cmath>
+#include <cstdlib>
+#include <set>
+#include <string>
+
+using namespace arm_compute;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output,
+ const int32_t block_size)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8,
+ DataType::S16, DataType::S32, DataType::F16,
+ DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8,
+ DataType::S16, DataType::S32, DataType::F16,
+ DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size >= 1,
+ "Block size should be greater than or equal to 1.");
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) == input->dimension(0) * block_size,
+ "Output width should be equal to (Input width * block size)");
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(1) == input->dimension(1) * block_size,
+ "Output height should be equal to (Input height * block size)");
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) % (block_size * block_size) == 0,
+ "Input depth should be divisible by (block size * block size)");
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+ output->dimension(2) == input->dimension(2) / (block_size * block_size),
+ "Output depth should be equal to (Input depth / (block size * block size))");
+
+ return Status{};
+}
+} // namespace
+
+CLDepthToSpaceKernel::CLDepthToSpaceKernel() : _input(nullptr), _output(nullptr)
+{
+ // DO NOTHING
+}
+
+void CLDepthToSpaceKernel::configure(const ICLTensor *input, ICLTensor *output,
+ const int32_t block_size)
+{
+
+ _input = input;
+ _output = output;
+
+ // Set kernel build options
+ std::set<std::string> build_opts;
+ build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ build_opts.emplace("-DBLOCK_SIZE=" + support::cpp11::to_string(block_size));
+ build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2)));
+
+ // Create kernel
+ _kernel =
+ static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("depth_to_space", build_opts));
+
+ // Configure kernel window
+ Window win = calculate_max_window(*output->info(), Steps());
+
+ Coordinates coord;
+ coord.set_num_dimensions(output->info()->num_dimensions());
+ output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
+
+ ICLKernel::configure(win);
+}
+
+void CLDepthToSpaceKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+
+ Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4);
+
+ // Setup input slice
+ Window slice_in(slice_out);
+ slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+ slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+ slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+ slice_in.set(3, Window::Dimension(0, 0, 0));
+
+ do
+ {
+ unsigned int idx = 0;
+ add_4D_tensor_argument(idx, _input, slice_in);
+ add_4D_tensor_argument(idx, _output, slice_out);
+ enqueue(queue, *this, slice_out);
+ } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out));
+}
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "arm_compute/runtime/CL/functions/CLDepthToSpace.h"
+
+#include "arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+void CLDepthToSpace::configure(ICLTensor *input, ICLTensor *output, const int32_t block_size)
+{
+ auto k = arm_compute::support::cpp14::make_unique<CLDepthToSpaceKernel>();
+ k->configure(input, output, block_size);
+ _kernel = std::move(k);
+}
#include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h>
#include <arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h>
#include <arm_compute/runtime/CL/functions/CLDequantizationLayer.h>
+#include <arm_compute/runtime/CL/functions/CLDepthToSpace.h>
#include <arm_compute/runtime/CL/functions/CLReductionMean.h>
#include <arm_compute/runtime/CL/functions/CLTranspose.h>
#include <arm_compute/runtime/CL/functions/CLRNNLayer.h>
auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) {
auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index});
auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index});
- auto rank = 4;
- auto fn = nnfw::make_unique<SimpleDepthToSpace>();
+ if (from_env<bool>(std::getenv("USE_SIMPLE_DEPTHTOSPACE")))
+ {
+ // USE CPU VERSION OF DEPTHTOSPACE
+ auto rank = 4;
+ auto fn = nnfw::make_unique<SimpleDepthToSpace>();
+
+ fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(rank));
- fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(rank));
+ builder.append("DepthToSpace", std::move(fn));
+ }
+ else
+ {
+ if (::internal::arm_compute::isGpuMode()) // GPU
+ {
+ auto fn = nnfw::make_unique<::arm_compute::CLDepthToSpace>();
- builder.append("DepthToSpace", std::move(fn));
+ fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), param.block_size);
+ builder.append("DepthToSpace", std::move(fn));
+ }
+ else // NEON
+ {
+ // TODO Enable NEON Support
+ throw std::runtime_error("Not supported, yet");
+ }
+ }
};
_builder.addStage(stage);
projects / platform / core / ml / nnfw.git / commitdiff