COMPMID-439 - Refactored NEQuantizationLayer and NEQuantizationLayer in order to support 3D input tensors
Change-Id: I03eac2108a30bed56d40dfd52e75577a35d492e0
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85783
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h
index 095a833..8f66b8a 100644
--- a/arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h
@@ -30,7 +30,11 @@
{
class ITensor;
-/** Interface for the dequantization layer kernel. */
+/** Interface for the dequantization layer kernel.
+ *
+ * @note The implementation supports only 3D input tensors
+ *
+ */
class NEDequantizationLayerKernel : public INEKernel
{
public:
@@ -48,12 +52,12 @@
~NEDequantizationLayerKernel() = default;
/** Set input, output, min and max.
*
- * @param[in] input Source tensor. Data types supported: U8.
- * @param[out] output Destination tensor. Data types supported: F32.
- * @param[in] min Minimum value of the input tensor.
- * @param[in] max Maximum value of the input tensor.
+ * @param[in] input Source tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data type supported: U8.
+ * @param[out] output Destination tensor with the same dimensions of input. Data type supported: F32.
+ * @param[in] min_max Pointer to the tensor with shape [2, batches] which stores the minimum and maximum value for each 3D input tensor.
+ * The dimensions over the second must match the batched dimensions of the input tensor. Data type supported: F32
*/
- void configure(const ITensor *input, ITensor *output, const float *min, const float *max);
+ void configure(const ITensor *input, ITensor *output, const ITensor *min_max);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
@@ -61,8 +65,7 @@
private:
const ITensor *_input;
ITensor *_output;
- const float *_min;
- const float *_max;
+ const ITensor *_min_max;
};
}
#endif /*__ARM_COMPUTE_NEDEQUANTIZATIONLAYERKERNEL_H__ */
diff --git a/arm_compute/core/NEON/kernels/NEMinMaxLayerKernel.h b/arm_compute/core/NEON/kernels/NEMinMaxLayerKernel.h
new file mode 100644
index 0000000..5e01acf
--- /dev/null
+++ b/arm_compute/core/NEON/kernels/NEMinMaxLayerKernel.h
@@ -0,0 +1,77 @@
+/*
+ * 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.
+ */
+
+#ifndef __ARM_COMPUTE_NEMINMAXLAYERKERNEL_H__
+#define __ARM_COMPUTE_NEMINMAXLAYERKERNEL_H__
+
+#include "arm_compute/core/NEON/INEKernel.h"
+
+#include <cstdint>
+#include <mutex>
+
+namespace arm_compute
+{
+class ITensor;
+
+/** Interface for the kernel to perform min max search on a 3D tensor. */
+class NEMinMaxLayerKernel : public INEKernel
+{
+public:
+ /** Default constructor */
+ NEMinMaxLayerKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEMinMaxLayerKernel(const NEMinMaxLayerKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEMinMaxLayerKernel &operator=(const NEMinMaxLayerKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ NEMinMaxLayerKernel(NEMinMaxLayerKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ NEMinMaxLayerKernel &operator=(NEMinMaxLayerKernel &&) = default;
+ /** Default destructor */
+ ~NEMinMaxLayerKernel() = default;
+
+ /** Initialise the kernel's input and outputs.
+ *
+ * @note output[0] = minimum
+ * @note output[1] = maximum
+ *
+ * @param[in] input Input tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data type supported: F32.
+ * @param[out] output Output tensor with shape [2, batches, ...] which stores the minimum and maximum value for each 3D input tensor.
+ * The dimensions over the second must match the batched dimensions of the input tensor. Data types supported: F32
+ */
+ void configure(const ITensor *input, ITensor *output);
+ /** Resets global minimum and maximum. */
+ void reset();
+
+ // Inherited methods overridden:
+ void run(const Window &window, const ThreadInfo &info) override;
+
+private:
+ void update_min_max(float *out_ptr, float min, float max);
+ const ITensor *_input;
+ ITensor *_output;
+ std::mutex _mtx;
+};
+}
+#endif /* __ARM_COMPUTE_NEMINMAXLAYERKERNEL_H__ */
\ No newline at end of file
diff --git a/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h
index 92cd142..617a2da 100644
--- a/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h
@@ -30,7 +30,11 @@
{
class ITensor;
-/** Interface for the quantization layer kernel. */
+/** Interface for the quantization layer kernel.
+ *
+ * @note The implementation supports only 3D input tensors
+ *
+ */
class NEQuantizationLayerKernel : public INEKernel
{
public:
@@ -48,12 +52,12 @@
~NEQuantizationLayerKernel() = default;
/** Set the input, output, min and max.
*
- * @param[in] input Source tensor. Data types supported: F32.
- * @param[out] output Destination tensor. Data types supported: U8.
- * @param[in] min Pointer to the minimum value of the input tensor.
- * @param[in] max Pointer to the maximum value of the input tensor.
+ * @param[in] input Source tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data types supported: F32.
+ * @param[out] output Destination tensor with the same dimensions of input. Data types supported: U8.
+ * @param[in] min_max Pointer to the tensor with shape [2, batches] which stores the minimum and maximum value for each 3D input tensor.
+ * The dimensions over the second must match the batched dimensions of the input tensor. Data type supported: F32
*/
- void configure(const ITensor *input, ITensor *output, const float *min, const float *max);
+ void configure(const ITensor *input, ITensor *output, const ITensor *min_max);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
@@ -61,8 +65,7 @@
private:
const ITensor *_input;
ITensor *_output;
- const float *_min;
- const float *_max;
+ const ITensor *_min_max;
};
}
#endif /*__ARM_COMPUTE_NEQUANTIZATIONLAYERKERNEL_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEDequantizationLayer.h b/arm_compute/runtime/NEON/functions/NEDequantizationLayer.h
index 7cd8360..8985861 100644
--- a/arm_compute/runtime/NEON/functions/NEDequantizationLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDequantizationLayer.h
@@ -27,7 +27,6 @@
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h"
-#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/core/Types.h"
@@ -37,6 +36,8 @@
/** Basic function to simulate a dequantization layer. This function calls the following NEON kernels:
*
+ * @note The implementation supports only 3D input tensors
+ *
* -# @ref NEDequantizationLayerKernel
*
*/
@@ -47,12 +48,12 @@
NEDequantizationLayer();
/** Configure the kernel.
*
- * @param[in] input Source tensor. Data types supported: U8.
- * @param[out] output Destination tensor. Data types supported: F32.
- * @param[in] min Minimum value of the input tensor.
- * @param[in] max Maximum value of the input tensor.
+ * @param[in] input Source tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data types supported: U8.
+ * @param[out] output Destination tensor with the same dimensions of input. Data type supported: F32.
+ * @param[in] min_max Pointer to the tensor with shape [2, batches] which stores the minimum and maximum value for each 3D input tensor.
+ * The dimensions over the second must match the batched dimensions of the input tensor. Data type supported: F32
*/
- void configure(const ITensor *input, ITensor *output, const float *min, const float *max);
+ void configure(const ITensor *input, ITensor *output, const ITensor *min_max);
// Inherited methods overridden:
void run() override;
diff --git a/arm_compute/runtime/NEON/functions/NEQuantizationLayer.h b/arm_compute/runtime/NEON/functions/NEQuantizationLayer.h
index ab189fe..d91b4ad 100644
--- a/arm_compute/runtime/NEON/functions/NEQuantizationLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEQuantizationLayer.h
@@ -26,7 +26,7 @@
#include "arm_compute/runtime/IFunction.h"
-#include "arm_compute/core/NEON/kernels/NEMinMaxLocationKernel.h"
+#include "arm_compute/core/NEON/kernels/NEMinMaxLayerKernel.h"
#include "arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h"
#include "arm_compute/runtime/Tensor.h"
@@ -38,7 +38,9 @@
/** Basic function to simulate a quantization layer. This function calls the following NEON kernels:
*
- * -# @ref NEMinMaxKernel
+ * @note The implementation supports only 3D input tensors
+ *
+ * -# @ref NEMinMaxLayerKernel
* -# @ref NEQuantizationLayerKernel
*
*/
@@ -49,8 +51,8 @@
NEQuantizationLayer();
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. Data types supported: F32
- * @param[out] output Destination tensor. Data types supported: U8
+ * @param[in] input Source tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data types supported: F32
+ * @param[out] output Destination tensor with the same dimensions of input. Data types supported: U8
*/
void configure(const ITensor *input, ITensor *output);
@@ -59,9 +61,8 @@
private:
NEQuantizationLayerKernel _quantize_kernel;
- NEMinMaxKernel _min_max_kernel;
- float _min;
- float _max;
+ NEMinMaxLayerKernel _min_max_kernel;
+ Tensor _min_max;
};
}
#endif /* __ARM_COMPUTE_NEQUANTIZATIONLAYER_H__ */
diff --git a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
index 3bf2b35..70984f0 100644
--- a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
@@ -23,9 +23,9 @@
*/
#include "arm_compute/core/NEON/kernels/NEDequantizationLayerKernel.h"
+#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
@@ -35,16 +35,16 @@
using namespace arm_compute;
NEDequantizationLayerKernel::NEDequantizationLayerKernel()
- : _input(nullptr), _output(nullptr), _min(nullptr), _max(nullptr)
+ : _input(nullptr), _output(nullptr), _min_max(nullptr)
{
}
-void NEDequantizationLayerKernel::configure(const ITensor *input, ITensor *output, const float *min, const float *max)
+void NEDequantizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *min_max)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON_NULLPTR(min);
- ARM_COMPUTE_ERROR_ON_NULLPTR(max);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(min_max);
+ ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() < 3);
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, DataType::F32, 0);
@@ -52,17 +52,20 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
- _input = input;
- _output = output;
- _min = min;
- _max = max;
+ _input = input;
+ _output = output;
+ _min_max = min_max;
constexpr unsigned int num_elems_processed_per_iteration = 8;
// Configure window
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+ AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
- update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration), output_access);
+ AccessWindowStatic min_max_access(min_max->info(), 0, 0, 2, min_max->info()->dimension(1));
+
+ // Update window and padding
+ update_window_and_padding(win, input_access, output_access, min_max_access);
output_access.set_valid_region(win, input->info()->valid_region());
INEKernel::configure(win);
@@ -74,31 +77,55 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- Iterator input(_input, window);
- Iterator output(_output, window);
+ Window window_input_output(window);
+ window_input_output.collapse_if_possible(INEKernel::window(), 3);
+ window_input_output.set(3, Window::Dimension(0, 1, 1));
- const float32x4_t vmin = vdupq_n_f32(*_min);
- const float range = *_max - *_min;
- const float32x4_t scaling = vdupq_n_f32(range / 255.0f);
+ Window window_min_max;
+ window_min_max.use_tensor_dimensions(_min_max->info()->tensor_shape());
+ window_min_max.set(Window::DimX, Window::Dimension(0, 1, 1));
+ window_min_max.collapse_if_possible(INEKernel::window(), 1);
- // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255]
- execute_window_loop(window, [&](const Coordinates & id)
+ Iterator input(_input, window_input_output);
+ Iterator output(_output, window_input_output);
+ Iterator min_max(_min_max, window_min_max);
+
+ execute_window_loop(window_min_max, [&](const Coordinates & id_batch)
{
- const uint8x8_t val_u8 = vld1_u8(reinterpret_cast<uint8_t *>(input.ptr()));
- const uint16x8_t val_u16 = vmovl_u8(val_u8);
- const uint32x4_t val_u32_low = vmovl_u16(vget_low_u16(val_u16));
- const uint32x4_t val_u32_high = vmovl_u16(vget_high_u16(val_u16));
- float32x4_t val_low = vcvtq_f32_u32(val_u32_low);
- float32x4_t val_high = vcvtq_f32_u32(val_u32_high);
+ // Get the min and max
+ const float min = *(reinterpret_cast<const float *>(min_max.ptr()) + 0);
+ const float max = *(reinterpret_cast<const float *>(min_max.ptr()) + 1);
- // Dequantize -> (q / 255.0 * range) + min
- val_low = vmulq_f32(val_low, scaling);
- val_high = vmulq_f32(val_high, scaling);
- val_low = vaddq_f32(val_low, vmin);
- val_high = vaddq_f32(val_high, vmin);
+ const float32x4_t vmin = vdupq_n_f32(min);
+ const float range = max - min;
+ const float32x4_t scaling = vdupq_n_f32(range / 255.0f);
- const float32x4x2_t dequantized = vuzpq_f32(val_low, val_high);
- vst2q_f32(reinterpret_cast<float *>(output.ptr()), dequantized);
+ // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255]
+ execute_window_loop(window_input_output, [&](const Coordinates & id)
+ {
+ // Get the input values
+ const auto input_ptr = reinterpret_cast<const uint8_t *>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]);
+
+ const uint8x8_t val_u8 = vld1_u8(input_ptr);
+ const uint16x8_t val_u16 = vmovl_u8(val_u8);
+ const uint32x4_t val_u32_low = vmovl_u16(vget_low_u16(val_u16));
+ const uint32x4_t val_u32_high = vmovl_u16(vget_high_u16(val_u16));
+ float32x4_t val_low = vcvtq_f32_u32(val_u32_low);
+ float32x4_t val_high = vcvtq_f32_u32(val_u32_high);
+
+ // Dequantize -> (q / 255.0 * range) + min
+ val_low = vmulq_f32(val_low, scaling);
+ val_high = vmulq_f32(val_high, scaling);
+ val_low = vaddq_f32(val_low, vmin);
+ val_high = vaddq_f32(val_high, vmin);
+
+ const float32x4x2_t dequantized = vuzpq_f32(val_low, val_high);
+
+ // Store the dequantized values
+ auto output_ptr = reinterpret_cast<float *>(output.ptr() + id_batch[1] * _output->info()->strides_in_bytes()[3]);
+ vst2q_f32(output_ptr, dequantized);
+ },
+ input, output);
},
- input, output);
-}
+ min_max);
+}
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp
new file mode 100644
index 0000000..5e6c48f
--- /dev/null
+++ b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp
@@ -0,0 +1,190 @@
+/*
+ * 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/core/NEON/kernels/NEMinMaxLayerKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <climits>
+#include <cstddef>
+
+namespace arm_compute
+{
+NEMinMaxLayerKernel::NEMinMaxLayerKernel()
+ : _input(nullptr), _output(nullptr), _mtx()
+{
+}
+
+void NEMinMaxLayerKernel::configure(const ITensor *input, ITensor *output)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() < 3);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ TensorShape output_shape{ input->info()->tensor_shape() };
+ output_shape.set(Window::DimX, 2);
+ output_shape.remove_dimension(1);
+ output_shape.remove_dimension(1);
+
+ // Output auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+ _input = input;
+ _output = output;
+
+ // Configure kernel window
+ constexpr unsigned int num_elems_processed_per_iteration = 1;
+
+ Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+ AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(output->info(), 0, 2);
+
+ update_window_and_padding(win, input_access, output_access);
+
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+ INEKernel::configure(win);
+}
+
+void NEMinMaxLayerKernel::run(const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+ const int x_start = window.x().start();
+ const int x_end = window.x().end();
+
+ Window window_output;
+ window_output.use_tensor_dimensions(_output->info()->tensor_shape());
+ window_output.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Handle X dimension manually to split into two loops
+ // First one will use vector operations, second one processes the left over pixels
+ Window window_input(window);
+ window_input.set(Window::DimX, Window::Dimension(0, 1, 1));
+ window_input.collapse_if_possible(INEKernel::window(), 3);
+ window_input.set(3, Window::Dimension(0, 1, 1));
+
+ Iterator input(_input, window_input);
+ Iterator output(_output, window_output);
+
+ execute_window_loop(window_output, [&](const Coordinates & id_batch)
+ {
+ float32x2_t carry_min = vdup_n_f32(std::numeric_limits<float>::max());
+ float32x2_t carry_max = vdup_n_f32(std::numeric_limits<float>::lowest());
+
+ float carry_min_scalar = std::numeric_limits<float>::max();
+ float carry_max_scalar = std::numeric_limits<float>::lowest();
+
+ execute_window_loop(window_input, [&](const Coordinates & id)
+ {
+ int x = x_start;
+ const auto in_ptr = reinterpret_cast<const float *const>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]);
+
+ // Vector loop
+ for(; x <= x_end - 8; x += 8)
+ {
+ const float32x4x2_t pixels = vld2q_f32(in_ptr + x);
+ const float32x4_t tmp_min1 = vminq_f32(pixels.val[0], pixels.val[1]);
+ const float32x4_t tmp_max1 = vmaxq_f32(pixels.val[0], pixels.val[1]);
+ const float32x2_t tmp_min2 = vmin_f32(vget_high_f32(tmp_min1), vget_low_f32(tmp_min1));
+ const float32x2_t tmp_max2 = vmax_f32(vget_high_f32(tmp_max1), vget_low_f32(tmp_max1));
+ carry_min = vmin_f32(tmp_min2, carry_min);
+ carry_max = vmax_f32(tmp_max2, carry_max);
+ }
+
+ // Process leftover pixels
+ for(; x < x_end; ++x)
+ {
+ const float pixel = in_ptr[x];
+ carry_min_scalar = std::min(pixel, carry_min_scalar);
+ carry_max_scalar = std::max(pixel, carry_max_scalar);
+ }
+ },
+ input);
+
+ // Reduce result
+ carry_min = vpmin_f32(carry_min, carry_min);
+ carry_max = vpmax_f32(carry_max, carry_max);
+ carry_min = vpmin_f32(carry_min, carry_min);
+ carry_max = vpmax_f32(carry_max, carry_max);
+
+ // Extract max/min values
+ const float min_i = std::min(vget_lane_f32(carry_min, 0), carry_min_scalar);
+ const float max_i = std::max(vget_lane_f32(carry_max, 0), carry_max_scalar);
+
+ auto out_ptr = reinterpret_cast<float *const>(output.ptr());
+
+ // Perform reduction of local min/max values
+ update_min_max(out_ptr, min_i, max_i);
+ },
+ output);
+}
+
+void NEMinMaxLayerKernel::reset()
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+
+ float32x2_t reset_values = vdup_n_f32(0.0f);
+ reset_values = vset_lane_f32(std::numeric_limits<float>::max(), reset_values, 0);
+ reset_values = vset_lane_f32(std::numeric_limits<float>::min(), reset_values, 1);
+
+ Window window_output;
+ window_output.use_tensor_dimensions(_output->info()->tensor_shape());
+ window_output.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator output(_output, window_output);
+
+ execute_window_loop(window_output, [&](const Coordinates & id)
+ {
+ vst1_f32(reinterpret_cast<float *const>(output.ptr()), reset_values);
+ },
+ output);
+}
+
+void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max)
+{
+ std::lock_guard<std::mutex> lock(_mtx);
+
+ const float32x2_t old_min = vld1_dup_f32(out_ptr);
+ const float32x2_t old_max = vld1_dup_f32(out_ptr + 1);
+ const float32x2_t new_min = vmin_f32(vdup_n_f32(min), old_min);
+ const float32x2_t new_max = vmax_f32(vdup_n_f32(max), old_max);
+
+ vst1_f32(out_ptr, vzip_f32(new_min, new_max).val[0]);
+}
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
index a596d83..bff79f0 100644
--- a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
@@ -23,9 +23,9 @@
*/
#include "arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h"
+#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
@@ -35,14 +35,15 @@
using namespace arm_compute;
NEQuantizationLayerKernel::NEQuantizationLayerKernel()
- : _input(nullptr), _output(nullptr), _min(nullptr), _max(nullptr)
+ : _input(nullptr), _output(nullptr), _min_max(nullptr)
{
}
-void NEQuantizationLayerKernel::configure(const ITensor *input, ITensor *output, const float *min, const float *max)
+void NEQuantizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *min_max)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+ ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() < 3);
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, DataType::U8, 0);
@@ -50,17 +51,20 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
- _input = input;
- _output = output;
- _min = min;
- _max = max;
+ _input = input;
+ _output = output;
+ _min_max = min_max;
constexpr unsigned int num_elems_processed_per_iteration = 8;
// Configure window
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+ AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
- update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration), output_access);
+ AccessWindowStatic min_max_access(min_max->info(), 0, 0, 2, min_max->info()->dimension(1));
+
+ // Update window and padding
+ update_window_and_padding(win, input_access, output_access, min_max_access);
output_access.set_valid_region(win, input->info()->valid_region());
INEKernel::configure(win);
@@ -72,36 +76,67 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- Iterator input(_input, window);
- Iterator output(_output, window);
+ Window window_input_output(window);
+ window_input_output.collapse_if_possible(INEKernel::window(), 3);
+ window_input_output.set(3, Window::Dimension(0, 1, 1));
- const float32x4_t vmin = vdupq_n_f32(*_min);
- const float32x4_t inv_range = vdupq_n_f32(1.0f / (*_max - *_min));
- const float32x4_t quantization_max = vdupq_n_f32(255.0f);
- const float32x4_t quantization_mul = vdupq_n_f32(256.0f);
+ Window window_min_max;
+ window_min_max.use_tensor_dimensions(_min_max->info()->tensor_shape());
+ window_min_max.set(Window::DimX, Window::Dimension(0, 1, 1));
+ window_min_max.collapse_if_possible(INEKernel::window(), 1);
- // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255]
- execute_window_loop(window, [&](const Coordinates & id)
+ Iterator input(_input, window_input_output);
+ Iterator output(_output, window_input_output);
+ Iterator min_max(_min_max, window_min_max);
+
+ execute_window_loop(window_min_max, [&](const Coordinates & id_batch)
{
- float32x4x2_t val = vld2q_f32(reinterpret_cast<const float *>(input.ptr()));
- // Map float values to range [0.0, 1.0]
- val.val[0] = vsubq_f32(val.val[0], vmin);
- val.val[1] = vsubq_f32(val.val[1], vmin);
- val.val[0] = vmulq_f32(val.val[0], inv_range);
- val.val[1] = vmulq_f32(val.val[1], inv_range);
+ // Get the min and max
+ float min = *(reinterpret_cast<const float *>(min_max.ptr()) + 0);
+ float max = *(reinterpret_cast<const float *>(min_max.ptr()) + 1);
- // Quantize
- val.val[0] = vmulq_f32(val.val[0], quantization_mul);
- val.val[1] = vmulq_f32(val.val[1], quantization_mul);
- val.val[0] = vminq_f32(val.val[0], quantization_max);
- val.val[1] = vminq_f32(val.val[1], quantization_max);
+ // Saturate the result if min = max
+ if(min == max)
+ {
+ min = 0.0f;
+ max = 1.0f;
+ }
- const uint32x4_t val_u32_low = vcvtq_u32_f32(val.val[0]);
- const uint32x4_t val_u32_high = vcvtq_u32_f32(val.val[1]);
- const uint16x4x2_t val_u16 = vzip_u16(vmovn_u32(val_u32_low), vmovn_u32(val_u32_high));
+ const float32x4_t vmin = vdupq_n_f32(min);
+ const float32x4_t inv_range = vdupq_n_f32(1.0f / (max - min));
+ const float32x4_t quantization_max = vdupq_n_f32(255.0f);
+ const float32x4_t quantization_mul = vdupq_n_f32(256.0f);
- const uint8x8_t quantized = vmovn_u16(vcombine_u16(val_u16.val[0], val_u16.val[1]));
- vst1_u8(reinterpret_cast<uint8_t *>(output.ptr()), quantized);
+ // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255]
+ execute_window_loop(window_input_output, [&](const Coordinates & id)
+ {
+ // Get the input values
+ const auto input_ptr = reinterpret_cast<const float *>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]);
+ float32x4x2_t val = vld2q_f32(input_ptr);
+
+ // Map float values to range [0.0, 1.0]
+ val.val[0] = vsubq_f32(val.val[0], vmin);
+ val.val[1] = vsubq_f32(val.val[1], vmin);
+ val.val[0] = vmulq_f32(val.val[0], inv_range);
+ val.val[1] = vmulq_f32(val.val[1], inv_range);
+
+ // Quantize
+ val.val[0] = vmulq_f32(val.val[0], quantization_mul);
+ val.val[1] = vmulq_f32(val.val[1], quantization_mul);
+ val.val[0] = vminq_f32(val.val[0], quantization_max);
+ val.val[1] = vminq_f32(val.val[1], quantization_max);
+
+ const uint32x4_t val_u32_low = vcvtq_u32_f32(val.val[0]);
+ const uint32x4_t val_u32_high = vcvtq_u32_f32(val.val[1]);
+ const uint16x4x2_t val_u16 = vzip_u16(vmovn_u32(val_u32_low), vmovn_u32(val_u32_high));
+
+ const uint8x8_t quantized = vmovn_u16(vcombine_u16(val_u16.val[0], val_u16.val[1]));
+
+ // Store the quantized values
+ auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr() + id_batch[1] * _output->info()->strides_in_bytes()[3]);
+ vst1_u8(output_ptr, quantized);
+ },
+ input, output);
},
- input, output);
+ min_max);
}
diff --git a/src/runtime/NEON/functions/NEDequantizationLayer.cpp b/src/runtime/NEON/functions/NEDequantizationLayer.cpp
index f174367..a58b6e4 100644
--- a/src/runtime/NEON/functions/NEDequantizationLayer.cpp
+++ b/src/runtime/NEON/functions/NEDequantizationLayer.cpp
@@ -24,9 +24,7 @@
#include "arm_compute/runtime/NEON/functions/NEDequantizationLayer.h"
-#include "arm_compute/core/Error.h"
#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
using namespace arm_compute;
@@ -36,13 +34,13 @@
{
}
-void NEDequantizationLayer::configure(const ITensor *input, ITensor *output, const float *min, const float *max)
+void NEDequantizationLayer::configure(const ITensor *input, ITensor *output, const ITensor *min_max)
{
- // Configure kernels
- _dequantize_kernel.configure(input, output, min, max);
+ // Configure kernel
+ _dequantize_kernel.configure(input, output, min_max);
}
void NEDequantizationLayer::run()
{
NEScheduler::get().schedule(&_dequantize_kernel, Window::DimY);
-}
+}
\ No newline at end of file
diff --git a/src/runtime/NEON/functions/NEQuantizationLayer.cpp b/src/runtime/NEON/functions/NEQuantizationLayer.cpp
index 46b9d7d..a131c48 100644
--- a/src/runtime/NEON/functions/NEQuantizationLayer.cpp
+++ b/src/runtime/NEON/functions/NEQuantizationLayer.cpp
@@ -30,17 +30,20 @@
using namespace arm_compute;
NEQuantizationLayer::NEQuantizationLayer()
- : _quantize_kernel(), _min_max_kernel(), _min(0.f), _max(0.f)
+ : _quantize_kernel(), _min_max_kernel(), _min_max()
{
}
void NEQuantizationLayer::configure(const ITensor *input, ITensor *output)
{
- // Configure min-max kernel
- _min_max_kernel.configure(input, &_min, &_max);
+ // Configure min-max kernel. _min_max tensor will be auto-configured within the kernel
+ _min_max_kernel.configure(input, &_min_max);
// Configure quantize kernel
- _quantize_kernel.configure(input, output, &_min, &_max);
+ _quantize_kernel.configure(input, output, &_min_max);
+
+ // Allocate min_max tensor
+ _min_max.allocator()->allocate();
}
void NEQuantizationLayer::run()
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index 4c449a7..806fc04 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -63,6 +63,36 @@
}
};
+/** Data set containing small 3D tensor shapes. */
+class Small3DShapes final : public ShapeDataset
+{
+public:
+ Small3DShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 7U, 7U, 5U },
+ TensorShape{ 27U, 13U, 37U },
+ TensorShape{ 128U, 64U, 21U }
+ })
+ {
+ }
+};
+
+/** Data set containing small 4D tensor shapes. */
+class Small4DShapes final : public ShapeDataset
+{
+public:
+ Small4DShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 7U, 7U, 5U, 3U },
+ TensorShape{ 27U, 13U, 37U, 2U },
+ TensorShape{ 128U, 64U, 21U, 3U }
+ })
+ {
+ }
+};
+
/** Data set containing small tensor shapes. */
class SmallShapes final : public ShapeDataset
{
@@ -117,6 +147,36 @@
}
};
+/** Data set containing large 3D tensor shapes. */
+class Large3DShapes final : public ShapeDataset
+{
+public:
+ Large3DShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 320U, 240U, 3U },
+ TensorShape{ 383U, 653U, 2U },
+ TensorShape{ 721U, 123U, 13U }
+ })
+ {
+ }
+};
+
+/** Data set containing large 4D tensor shapes. */
+class Large4DShapes final : public ShapeDataset
+{
+public:
+ Large4DShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 320U, 123U, 3U, 3U },
+ TensorShape{ 383U, 413U, 2U, 3U },
+ TensorShape{ 517U, 123U, 13U, 2U }
+ })
+ {
+ }
+};
+
/** Data set containing small tensor shapes for direct convolution. */
class SmallDirectConvolutionShapes final : public ShapeDataset
{
diff --git a/tests/validation/CPP/DequantizationLayer.cpp b/tests/validation/CPP/DequantizationLayer.cpp
index 1c7ec25..33096a1 100644
--- a/tests/validation/CPP/DequantizationLayer.cpp
+++ b/tests/validation/CPP/DequantizationLayer.cpp
@@ -32,23 +32,35 @@
namespace reference
{
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
-SimpleTensor<float> dequantization_layer(const SimpleTensor<T> &src, float min, float max)
+SimpleTensor<float> dequantization_layer(const SimpleTensor<T> &src, const SimpleTensor<float> &min_max)
{
// Create reference
SimpleTensor<float> dst{ src.shape(), DataType::F32 };
- const float range = max - min;
- const float scaling = range / 255.0f;
+ // Compute reference
+ const int width = src.shape().x();
+ const int height = src.shape().y();
+ const int depth = src.shape().z();
+ const int stride_w = width * height * depth;
+ const int num_batches = min_max.shape().total_size_upper(1);
- for(int i = 0; i < src.num_elements(); ++i)
+ for(int k = 0; k < num_batches; ++k)
{
- dst[i] = (static_cast<float>(src[i]) * scaling) + min;
+ const float min = min_max[k * 2 + 0];
+ const float max = min_max[k * 2 + 1];
+ const float range = max - min;
+ const float scaling = range / 255.0f;
+
+ for(int i = 0; i < stride_w; ++i)
+ {
+ dst[i + k * stride_w] = (static_cast<float>(src[i + k * stride_w]) * scaling) + min;
+ }
}
return dst;
}
-template SimpleTensor<float> dequantization_layer(const SimpleTensor<uint8_t> &src, float min, float max);
+template SimpleTensor<float> dequantization_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<float> &min_max);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/CPP/DequantizationLayer.h b/tests/validation/CPP/DequantizationLayer.h
index 3aae338..1a8adcf 100644
--- a/tests/validation/CPP/DequantizationLayer.h
+++ b/tests/validation/CPP/DequantizationLayer.h
@@ -36,7 +36,7 @@
namespace reference
{
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-SimpleTensor<float> dequantization_layer(const SimpleTensor<T> &src, float min, float max);
+SimpleTensor<float> dequantization_layer(const SimpleTensor<T> &src, const SimpleTensor<float> &min_max);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/CPP/QuantizationLayer.cpp b/tests/validation/CPP/QuantizationLayer.cpp
index d61e75a..0584d88 100644
--- a/tests/validation/CPP/QuantizationLayer.cpp
+++ b/tests/validation/CPP/QuantizationLayer.cpp
@@ -60,19 +60,48 @@
// Create reference
SimpleTensor<uint8_t> dst{ src.shape(), DataType::U8 };
- // Compute min and max of the tensor using Min-Max layer
- float min = 0.f;
- float max = 0.f;
+ const int width = src.shape().x();
+ const int height = src.shape().y();
+ const int depth = src.shape().z();
+ const int stride_w = width * height * depth;
+ const int num_batches = src.shape().total_size_upper(3);
- compute_min_max(src, &min, &max);
-
- const float range = max - min;
-
- for(int i = 0; i < src.num_elements(); ++i)
+ for(int k = 0; k < num_batches; ++k)
{
- // map values to range [0.0, 1.0]
- const float normalized = (src[i] - min) / range;
- dst[i] = static_cast<uint8_t>(std::min(255.0f, normalized * 256.0f));
+ // Compute min and max of the 3D tensor
+ float min = src[0];
+ float max = src[0];
+
+ // Look for min and max values
+ for(int i = 1; i < stride_w; ++i)
+ {
+ float val = src[i + k * stride_w];
+ if(val < min)
+ {
+ min = val;
+ }
+ if(val > max)
+ {
+ max = val;
+ }
+ }
+
+ // Saturate the result in case min = max
+ if(min == max)
+ {
+ min = 0.0f;
+ max = 1.0f;
+ }
+
+ const float range = max - min;
+
+ for(int i = 0; i < stride_w; ++i)
+ {
+ // map values to range [0.0, 1.0]
+ float val = src[i + k * stride_w];
+ const float normalized = (val - min) / range;
+ dst[i + k * stride_w] = static_cast<uint8_t>(std::min(255.0f, normalized * 256.0f));
+ }
}
return dst;
diff --git a/tests/validation/NEON/DequantizationLayer.cpp b/tests/validation/NEON/DequantizationLayer.cpp
index 22d56ab..9bdba72 100644
--- a/tests/validation/NEON/DequantizationLayer.cpp
+++ b/tests/validation/NEON/DequantizationLayer.cpp
@@ -44,35 +44,56 @@
{
/** Tolerance for float operations */
constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
+
+const auto DequantizationShapes = concat(concat(concat(datasets::Small3DShapes(),
+ datasets::Large3DShapes()),
+ datasets::Small4DShapes()),
+ datasets::Large4DShapes());
+
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(DequantizationLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(concat(datasets::Small2DShapes(), datasets::Large2DShapes()), framework::dataset::make("DataType", DataType::U8)), shape, data_type)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(DequantizationShapes, framework::dataset::make("DataType", DataType::U8)), shape, data_type)
{
+ TensorShape shape_min_max = shape;
+ shape_min_max.set(Window::DimX, 2);
+
+ // Remove Y and Z dimensions and keep the batches
+ shape_min_max.remove_dimension(1);
+ shape_min_max.remove_dimension(1);
+
// Create tensors
- Tensor src = create_tensor<Tensor>(shape, data_type);
- Tensor dst = create_tensor<Tensor>(shape, DataType::F32);
+ Tensor src = create_tensor<Tensor>(shape, data_type);
+ Tensor dst = create_tensor<Tensor>(shape, DataType::F32);
+ Tensor min_max = create_tensor<Tensor>(shape_min_max, DataType::F32);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(min_max.info()->is_resizable(), framework::LogLevel::ERRORS);
// Create and configure function
- float min = 0.f;
- float max = 0.f;
NEDequantizationLayer dequant_layer;
- dequant_layer.configure(&src, &dst, &min, &max);
+ dequant_layer.configure(&src, &dst, &min_max);
// Validate valid region
const ValidRegion valid_region = shape_to_valid_region(shape);
validate(src.info()->valid_region(), valid_region);
validate(dst.info()->valid_region(), valid_region);
+ // Validate valid region of min_max tensor
+ const ValidRegion valid_region_min_max = shape_to_valid_region(shape_min_max);
+ validate(min_max.info()->valid_region(), valid_region_min_max);
+
// Validate padding
const PaddingSize padding = PaddingCalculator(shape.x(), 8).required_padding();
validate(src.info()->padding(), padding);
validate(dst.info()->padding(), padding);
+
+ // Validate padding of min_max tensor
+ const PaddingSize padding_min_max = PaddingCalculator(shape_min_max.x(), 2).required_padding();
+ validate(min_max.info()->padding(), padding_min_max);
}
template <typename T>
@@ -80,12 +101,14 @@
TEST_SUITE(Integer)
TEST_SUITE(U8)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEDequantizationLayerFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::Small2DShapes(), framework::dataset::make("DataType", DataType::U8)))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDequantizationLayerFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(concat(datasets::Small3DShapes(), datasets::Small4DShapes()),
+ framework::dataset::make("DataType", DataType::U8)))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEDequantizationLayerFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(datasets::Large2DShapes(), framework::dataset::make("DataType", DataType::U8)))
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDequantizationLayerFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(concat(datasets::Large3DShapes(), datasets::Large4DShapes()),
+ framework::dataset::make("DataType", DataType::U8)))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
diff --git a/tests/validation/NEON/QuantizationLayer.cpp b/tests/validation/NEON/QuantizationLayer.cpp
index 5c2fab4..26657c4 100644
--- a/tests/validation/NEON/QuantizationLayer.cpp
+++ b/tests/validation/NEON/QuantizationLayer.cpp
@@ -44,12 +44,17 @@
{
/** Tolerance for quantization */
constexpr AbsoluteTolerance<uint8_t> tolerance_u8(1);
+
+const auto QuantizationShapes = concat(concat(concat(datasets::Small3DShapes(),
+ datasets::Large3DShapes()),
+ datasets::Small4DShapes()),
+ datasets::Large4DShapes());
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(QuantizationLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(concat(datasets::Small2DShapes(), datasets::Large2DShapes()), framework::dataset::make("DataType", DataType::F32)), shape, data_type)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(QuantizationShapes, framework::dataset::make("DataType", DataType::F32)), shape, data_type)
{
// Create tensors
Tensor src = create_tensor<Tensor>(shape, data_type);
@@ -78,12 +83,14 @@
TEST_SUITE(Float)
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEQuantizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::Small2DShapes(), framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEQuantizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(concat(datasets::Small3DShapes(), datasets::Small4DShapes()),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_u8);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEQuantizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::Large2DShapes(), framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLarge, NEQuantizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(concat(datasets::Large3DShapes(), datasets::Large4DShapes()),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_u8);
diff --git a/tests/validation/fixtures/DequantizationLayerFixture.h b/tests/validation/fixtures/DequantizationLayerFixture.h
index 7543eb2..28d43cf 100644
--- a/tests/validation/fixtures/DequantizationLayerFixture.h
+++ b/tests/validation/fixtures/DequantizationLayerFixture.h
@@ -49,11 +49,8 @@
template <typename...>
void setup(TensorShape shape, DataType data_type)
{
- // Initialize random min and max values
- rand_min_max(&_min, &_max);
-
- _target = compute_target(shape, data_type, _min, _max);
- _reference = compute_reference(shape, data_type, _min, _max);
+ _target = compute_target(shape, data_type);
+ _reference = compute_reference(shape, data_type);
}
protected:
@@ -63,28 +60,80 @@
library->fill_tensor_uniform(tensor, 0);
}
- TensorType compute_target(const TensorShape &shape, DataType data_type, float min, float max)
+ template <typename U>
+ void fill_min_max(U &&tensor)
{
+ std::mt19937 gen(library->seed());
+ std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
+
+ Window window;
+
+ window.set(0, Window::Dimension(0, tensor.shape()[0], 2));
+
+ for(unsigned int d = 1; d < tensor.shape().num_dimensions(); ++d)
+ {
+ window.set(d, Window::Dimension(0, tensor.shape()[d], 1));
+ }
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ const float n1 = distribution(gen);
+ const float n2 = distribution(gen);
+
+ float min = 0.0f;
+ float max = 0.0f;
+
+ if(n1 < n2)
+ {
+ min = n1;
+ max = n2;
+ }
+ else
+ {
+ min = n2;
+ max = n1;
+ }
+
+ auto out_ptr = reinterpret_cast<float *>(tensor(id));
+ out_ptr[0] = min;
+ out_ptr[1] = max;
+ });
+ }
+
+ TensorType compute_target(const TensorShape &shape, DataType data_type)
+ {
+ TensorShape shape_min_max = shape;
+ shape_min_max.set(Window::DimX, 2);
+
+ // Remove Y and Z dimensions and keep the batches
+ shape_min_max.remove_dimension(1);
+ shape_min_max.remove_dimension(1);
+
// Create tensors
- TensorType src = create_tensor<TensorType>(shape, data_type);
- TensorType dst = create_tensor<TensorType>(shape, DataType::F32);
+ TensorType src = create_tensor<TensorType>(shape, data_type);
+ TensorType dst = create_tensor<TensorType>(shape, DataType::F32);
+ TensorType min_max = create_tensor<TensorType>(shape_min_max, DataType::F32);
// Create and configure function
FunctionType dequantization_layer;
- dequantization_layer.configure(&src, &dst, &min, &max);
+ dequantization_layer.configure(&src, &dst, &min_max);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(min_max.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
+ min_max.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!min_max.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src));
+ fill_min_max(AccessorType(min_max));
// Compute function
dequantization_layer.run();
@@ -92,43 +141,28 @@
return dst;
}
- SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type, float min, float max)
+ SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type)
{
+ TensorShape shape_min_max = shape;
+ shape_min_max.set(Window::DimX, 2);
+
+ // Remove Y and Z dimensions and keep the batches
+ shape_min_max.remove_dimension(1);
+ shape_min_max.remove_dimension(1);
+
// Create reference
- SimpleTensor<T> src{ shape, data_type };
+ SimpleTensor<T> src{ shape, data_type };
+ SimpleTensor<float> min_max{ shape_min_max, data_type };
// Fill reference
fill(src);
+ fill_min_max(min_max);
- return reference::dequantization_layer<T>(src, min, max);
- }
-
- /** Generate random constant values to be used as min and max for dequantization.
- */
- void rand_min_max(float *min, float *max)
- {
- std::mt19937 gen(library->seed());
- std::uniform_real_distribution<float> distribution(-10000.0, 10000.0);
-
- const float n1 = distribution(gen);
- const float n2 = distribution(gen);
-
- if(n1 < n2)
- {
- *min = n1;
- *max = n2;
- }
- else
- {
- *min = n2;
- *max = n1;
- }
+ return reference::dequantization_layer<T>(src, min_max);
}
TensorType _target{};
SimpleTensor<float> _reference{};
- float _min = 0.f;
- float _max = 0.f;
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>