/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * 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.
- */
-
-/*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-
#include "arm_compute/runtime/NEON/functions/NETransposeConvLayer.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Utils.h"
#include "arm_compute/core/UtilsEx.h"
#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/misc/ShapeCalculatorEx.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
namespace arm_compute
{
+
NETransposeConvLayer::NETransposeConvLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_conv_f(),
_upsample_f(),
_flip_weights(),
- _permute_input(),
- _permute_weights(),
- _permute_output(),
_scaled_output(),
_weights_flipped(),
- _permuted_input(),
- _permuted_weights(),
- _permuted_output(),
- _is_nchw(false),
+ _flip_axis(),
_original_weights(nullptr),
_input(nullptr),
_info(),
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16,
- DataType::QASYMM8);
+ DataType::QASYMM8, DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, input);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(weights, input);
const unsigned int width_idx =
weights->dimension(height_idx), info, invalid_right, invalid_bottom);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- if (is_data_type_quantized_asymmetric(input->data_type()) && bias)
+ if (bias != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
- }
- else if (bias)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ if (is_data_type_quantized_asymmetric(input->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ }
}
if (output->tensor_shape().total_size() > 0)
const TensorShape output_shape = compute_transposeconv_output_shape(out_dims, *input, *weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) < output_shape.x(),
- "Output's dim 0 is invalid.");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) < output_shape.y(),
- "Output's dim 1 is invalid.");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) < output_shape.z(),
- "Output's dim 2 is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(),
+ "Output's width is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(),
+ "Output's height is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(),
+ "Output's depth is invalid.");
}
unsigned int pad_left = 0;
pad_bottom);
TensorInfo scale_out_info(
input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape));
- scale_out_info.set_data_layout(input->data_layout());
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
const unsigned int batches_idx =
ITensor *output, const PadStrideInfo &info,
unsigned int invalid_right, unsigned int invalid_bottom)
{
+ // Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(NETransposeConvLayer::validate(
+ input->info(), weights->info(), (bias == nullptr) ? nullptr : bias->info(), output->info(),
+ info, invalid_right, invalid_bottom));
const DataLayout data_layout = input->info()->data_layout();
-
- _input = input;
- _original_weights = weights;
- _info = info;
- _is_prepared = false;
- _is_nchw = data_layout == DataLayout::NCHW;
-
- const unsigned int stride_x = info.stride().first;
- const unsigned int stride_y = info.stride().second;
-
const unsigned int width_idx =
get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int height_idx =
const TensorShape output_shape =
compute_transposeconv_output_shape(out_dims, *input->info(), *weights->info());
+
+ _input = input;
+ _original_weights = weights;
+ _info = info;
+ _is_prepared = false;
+
+ unsigned int pad_left = 0;
+ unsigned int pad_right = 0;
+ unsigned int pad_top = 0;
+ unsigned int pad_bottom = 0;
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+
// Output auto initialization if not yet initialized
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(),
input->info()->quantization_info());
- // Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(NETransposeConvLayer::validate(
- input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(),
- info, invalid_right, invalid_bottom));
-
+ _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32));
_memory_group.manage(&_scaled_output);
- if (!_is_nchw)
- {
- _memory_group.manage(&_permuted_input);
- _memory_group.manage(&_permuted_weights);
- _memory_group.manage(&_permuted_output);
-
- // Configure the function to transform the input tensor from NHWC -> NCHW
- _permuted_input.info()->set_quantization_info(input->info()->quantization_info());
- _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
- _permuted_input.info()->set_data_layout(DataLayout::NCHW);
-
- // Configure the function to transform the weights tensor from NHWC -> NCHW
- _permuted_weights.info()->set_quantization_info(weights->info()->quantization_info());
- _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
- _permuted_weights.info()->set_data_layout(DataLayout::NCHW);
-
- // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in
- // order to match output shape
-
- unsigned int pad_left = 0;
- unsigned int pad_right = 0;
- unsigned int pad_top = 0;
- unsigned int pad_bottom = 0;
- const TensorShape scale_out_shape = compute_transposeconv_upsampled_shape(
- *_permuted_input.info(), *_permuted_weights.info(), info, out_dims, invalid_right,
- invalid_bottom, pad_left, pad_right, pad_top, pad_bottom);
-
- TensorInfo scale_out_info(scale_out_shape, 1, _permuted_input.info()->data_type(),
- _permuted_input.info()->quantization_info());
- scale_out_info.set_data_layout(DataLayout::NCHW);
- _scaled_output.allocator()->init(scale_out_info);
-
- const PadStrideInfo upsample_info(stride_x, stride_y, pad_left, pad_right, pad_top, pad_bottom,
- DimensionRoundingType::CEIL);
- _upsample_f.configure(&_permuted_input, &_scaled_output, upsample_info);
-
- _weights_flipped.allocator()->init(*_permuted_weights.info()->clone());
- _weights_flipped.info()->set_quantization_info(weights->info()->quantization_info());
- _flip_weights.configure(&_permuted_weights, &_weights_flipped);
-
- // setup the function to convolve the upscaled output
- const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
-
- const auto out_shape = output->info()->tensor_shape();
- TensorShape permuted_out_shape{out_shape[1], out_shape[2], out_shape[0], out_shape[3]};
- TensorInfo permuted_out_info(permuted_out_shape, 1, output->info()->data_type(),
- output->info()->quantization_info());
- _permuted_output.allocator()->init(permuted_out_info);
- _permuted_output.info()->set_data_layout(DataLayout::NCHW);
- _conv_f.configure(&_scaled_output, &_weights_flipped, bias, &_permuted_output, conv_info);
-
- // Configure the function to transform the convoluted output to NHWC
- _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U));
-
- _permuted_input.allocator()->allocate();
- _permuted_weights.allocator()->allocate();
- _permuted_output.allocator()->allocate();
- }
- else
- {
- // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in
- // order to match output shape
- unsigned int pad_left = 0;
- unsigned int pad_right = 0;
- unsigned int pad_top = 0;
- unsigned int pad_bottom = 0;
- const TensorShape scale_out_shape = compute_transposeconv_upsampled_shape(
- *input->info(), *weights->info(), info, out_dims, invalid_right, invalid_bottom, pad_left,
- pad_right, pad_top, pad_bottom);
-
- TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(),
- input->info()->quantization_info());
- _scaled_output.allocator()->init(scale_out_info);
- const PadStrideInfo upsample_info(stride_x, stride_y, pad_left, pad_right, pad_top, pad_bottom,
- DimensionRoundingType::FLOOR);
- _upsample_f.configure(input, &_scaled_output, upsample_info);
-
- _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
- _flip_weights.configure(weights, &_weights_flipped);
-
- // setup the function to convolve the upscaled output
- const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
- _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info);
- }
+ _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
+ _flip_weights.configure(weights, &_weights_flipped, &_flip_axis);
+
+ // setup the function to convolve the upscaled output
+ const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+ const TensorShape scale_out_shape = compute_transposeconv_upsampled_shape(
+ *input->info(), *weights->info(), info, out_dims, invalid_right, invalid_bottom, pad_left,
+ pad_right, pad_top, pad_bottom);
+
+ const PadStrideInfo upsample_info(stride_x, stride_y, pad_left, pad_right, pad_top, pad_bottom,
+ DimensionRoundingType::FLOOR);
+
+ TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(),
+ input->info()->quantization_info());
+ scale_out_info.set_data_layout(data_layout);
+ _scaled_output.allocator()->init(scale_out_info);
+
+ _upsample_f.configure(input, &_scaled_output, upsample_info);
+
+ _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info);
+
+ // Setup flip axis data
+ _flip_axis.allocator()->allocate();
+ auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
+ axis_data[0] = static_cast<uint32_t>(width_idx);
+ axis_data[1] = static_cast<uint32_t>(height_idx);
+
_scaled_output.allocator()->allocate();
}
{
prepare();
- // MemoryGroupResourceScope scope_mg(_memory_group);
-
- // Permute input
- if (!_is_nchw)
- {
- _permute_input.run();
- }
+ MemoryGroupResourceScope scope_mg(_memory_group);
_upsample_f.run();
_conv_f.run();
-
- // Permute output
- if (!_is_nchw)
- {
- _permute_output.run();
- }
}
void NETransposeConvLayer::prepare()
// Run weights flipping and mark original weights tensor as unused
_weights_flipped.allocator()->allocate();
- // Permute weights
- if (!_is_nchw)
- {
- _permute_weights.run();
- }
- NEScheduler::get().schedule(&_flip_weights, Window::DimZ);
+ _flip_weights.run();
_original_weights->mark_as_unused();
// Prepare convolution
_conv_f.prepare();
- if (!_weights_flipped.is_used())
- {
- _weights_flipped.allocator()->free();
- }
-
_is_prepared = true;
}
}
projects / platform / core / ml / nnfw.git / blobdiff