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diff --git a/compute/ARMComputeEx/src/runtime/CL/functions/CLDirectTransposeConvLayer.cpp b/compute/ARMComputeEx/src/runtime/CL/functions/CLDirectTransposeConvLayer.cpp
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+++ b/compute/ARMComputeEx/src/runtime/CL/functions/CLDirectTransposeConvLayer.cpp
@@ -0,0 +1,267 @@
+/*
+ * Copyright (c) 2020 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) 2019-2020 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/CL/functions/CLDirectTransposeConvLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/UtilsEx.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculatorEx.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <memory>
+#include <tuple>
+
+namespace arm_compute
+{
+using namespace arm_compute::misc::shape_calculator;
+
+CLDirectTransposeConvLayer::CLDirectTransposeConvLayer(
+    std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
+    : _memory_group(std::move(memory_manager)),
+      _scale_f(),
+      _conv_f(),
+      _flip_weights(),
+      _scaled_output(),
+      _original_weights(nullptr),
+      _weights_flipped(),
+      _flip_axis(),
+      _is_prepared(false)
+{
+}
+
+Status CLDirectTransposeConvLayer::validate(const ITensorInfo *input, const ITensorInfo *weights,
+                                            const ITensorInfo *bias, ITensorInfo *output,
+                                            const PadStrideInfo &info, unsigned int invalid_right,
+                                            unsigned int invalid_bottom,
+                                            const WeightsInfo &weights_info)
+{
+  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(
+      input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
+  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+  const DataLayout data_layout = input->data_layout();
+
+  const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+  const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+  const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+  ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h));
+  ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1);
+
+  auto out_dims = transposeconv_output_dimensions(
+      input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w),
+      weights->dimension(idx_h), info, invalid_right, invalid_bottom);
+
+  const TensorShape output_shape = compute_transposeconv_output_shape(out_dims, *input, *weights);
+
+  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
+
+  if (bias != nullptr)
+  {
+    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);
+    }
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias);
+  }
+
+  ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w],
+                                  "Output's width is invalid.");
+  ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h],
+                                  "Output's height is invalid.");
+  ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c],
+                                  "Output's depth is invalid.");
+
+  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, *weights, info, out_dims, invalid_right, invalid_bottom, pad_left, pad_right, pad_top,
+      pad_bottom);
+  TensorInfo scale_out_info(input->clone()
+                                ->set_is_resizable(true)
+                                .reset_padding()
+                                .set_tensor_shape(scale_out_shape)
+                                .set_data_layout(data_layout));
+  const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+  ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, info));
+  ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output,
+                                                           conv_info, weights_info));
+
+  return Status{};
+}
+
+void CLDirectTransposeConvLayer::configure(ICLTensor *input, ICLTensor *weights,
+                                           const ICLTensor *bias, ICLTensor *output,
+                                           const PadStrideInfo &info, unsigned int invalid_right,
+                                           unsigned int invalid_bottom,
+                                           const WeightsInfo &weights_info)
+{
+  configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, info,
+            invalid_right, invalid_bottom, weights_info);
+}
+
+void CLDirectTransposeConvLayer::configure(const CLCompileContext &compile_context,
+                                           ICLTensor *input, ICLTensor *weights,
+                                           const ICLTensor *bias, ICLTensor *output,
+                                           const PadStrideInfo &info, unsigned int invalid_right,
+                                           unsigned int invalid_bottom,
+                                           const WeightsInfo &weights_info)
+{
+  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+  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;
+
+  const DataLayout data_layout = input->info()->data_layout();
+
+  const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+  const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+  _original_weights = weights;
+  _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32));
+  _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
+  _flip_weights.configure(compile_context, weights, &_weights_flipped, &_flip_axis);
+
+  auto out_dims = transposeconv_output_dimensions(
+      input->info()->dimension(idx_w), input->info()->dimension(idx_h),
+      weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), info, invalid_right,
+      invalid_bottom);
+
+  const TensorShape output_shape =
+      compute_transposeconv_output_shape(out_dims, *input->info(), *weights->info());
+
+  // Output auto initialization if not yet initialized
+  auto_init_if_empty(
+      *output->info(),
+      input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout));
+
+  // Perform validation step
+  ARM_COMPUTE_ERROR_THROW_ON(CLDirectTransposeConvLayer::validate(
+      input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(),
+      info, invalid_right, invalid_bottom));
+
+  _is_prepared = weights_info.retain_internal_weights();
+
+  _memory_group.manage(&_scaled_output);
+
+  // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order
+  // to match output shape
+  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());
+  scale_out_info.set_data_layout(data_layout);
+  _scaled_output.allocator()->init(scale_out_info);
+
+  // configure scale function
+  const PadStrideInfo upsample_info(stride_x, stride_y, pad_left, pad_right, pad_top, pad_bottom,
+                                    DimensionRoundingType::FLOOR);
+  _scale_f.configure(input, &_scaled_output, upsample_info);
+
+  // Setup the function to convolve the upscaled output
+  const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+  _conv_f.configure(compile_context, &_scaled_output, &_weights_flipped, bias, output, conv_info,
+                    weights_info);
+  _scaled_output.allocator()->allocate();
+
+  // Setup flip axis data
+  _flip_axis.allocator()->allocate();
+  _flip_axis.map(true);
+  auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
+  if (weights->info()->data_layout() == DataLayout::NHWC)
+  {
+    axis_data[0] = 1;
+    axis_data[1] = 2;
+  }
+  else
+  {
+    axis_data[0] = 0;
+    axis_data[1] = 1;
+  }
+  _flip_axis.unmap();
+}
+
+void CLDirectTransposeConvLayer::run()
+{
+  prepare();
+
+  MemoryGroupResourceScope scope_mg(_memory_group);
+
+  _scale_f.run();
+  _conv_f.run();
+}
+
+void CLDirectTransposeConvLayer::prepare()
+{
+  if (!_is_prepared)
+  {
+    ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+    // Run weights flipping and mark original weights tensor as unused
+    _weights_flipped.allocator()->allocate();
+    _flip_weights.run();
+    _original_weights->mark_as_unused();
+
+    // Prepare convolution
+    _conv_f.prepare();
+
+    // Free flipped weights
+    if (!_weights_flipped.is_used())
+    {
+      _weights_flipped.allocator()->free();
+    }
+
+    _is_prepared = true;
+  }
+}
+} // namespace arm_compute