Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[onert-micro] Add Conv2D kernel #12740

Merged
merged 1 commit into from
Mar 12, 2024
Merged

[onert-micro] Add Conv2D kernel #12740

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
119 changes: 119 additions & 0 deletions onert-micro/onert-micro/include/pal/common/PALConv2DCommon.h
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,119 @@
/*
* Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/

#ifndef ONERT_MICRO_PAL_CONV2D_COMMON_H
#define ONERT_MICRO_PAL_CONV2D_COMMON_H

#include "PALUtils.h"

#include "OMStatus.h"

namespace onert_micro
{
namespace execute
{
namespace pal
{
OMStatus ConvFloat(const core::FloatConv2D *params, const core::OMRuntimeShape &input_shape,
const float *input_data, const core::OMRuntimeShape &filter_shape,
const float *filter_data, const float *bias_data,
const core::OMRuntimeShape &output_shape, float *output_data)
{
const int stride_width = params->stride_w;
const int stride_height = params->stride_h;
const int dilation_width_factor = params->dilation_width_factor;
const int dilation_height_factor = params->dilation_height_factor;
const int pad_width = params->pad_w;
const int pad_height = params->pad_h;
const float output_activation_min = params->activation_min;
const float output_activation_max = params->activation_max;

const auto batches = input_shape.dims(0);
const int input_height = input_shape.dims(1);
const int input_width = input_shape.dims(2);
const int input_depth = input_shape.dims(3);
const int output_depth = filter_shape.dims(0);
const int filter_height = filter_shape.dims(1);
const int filter_width = filter_shape.dims(2);
const int output_height = output_shape.dims(1);
const int output_width = output_shape.dims(2);
for (int batch = 0; batch < batches; ++batch)
{
for (int out_y = 0; out_y < output_height; ++out_y)
{
const int in_y_origin = (out_y * stride_height) - pad_height;
for (int out_x = 0; out_x < output_width; ++out_x)
{
const int in_x_origin = (out_x * stride_width) - pad_width;
for (int out_channel = 0; out_channel < output_depth; ++out_channel)
{
float total = 0.f;
for (int filter_y = 0; filter_y < filter_height; ++filter_y)
{
const int in_y = in_y_origin + dilation_height_factor * filter_y;
for (int filter_x = 0; filter_x < filter_width; ++filter_x)
{
const int in_x = in_x_origin + dilation_width_factor * filter_x;

// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height);

if (!is_point_inside_image)
{
continue;
}

for (int in_channel = 0; in_channel < input_depth; ++in_channel)
{
const int input_data_offset =
((batch * input_height + in_y) * input_width + in_x) * input_depth + in_channel;

const int filter_data_offset =
((out_channel * filter_height + filter_y) * filter_width + filter_x) *
input_depth +
in_channel;

const float input_value = input_data[input_data_offset];
const float filter_value = filter_data[filter_data_offset];
total += (input_value * filter_value);
}
}
}
// float bias_value = 0.0f;
if (bias_data)
{
total += bias_data[out_channel];
}

const int output_data_offset =
((batch * output_height + out_y) * output_width + out_x) * output_depth + out_channel;

output_data[output_data_offset] =
std::min(std::max(total, output_activation_min), output_activation_max);
}
}
}
}
return Ok;
}

} // namespace pal
} // namespace execute
} // namespace onert_micro

#endif // ONERT_MICRO_PAL_CONV2D_COMMON_H
22 changes: 22 additions & 0 deletions onert-micro/onert-micro/include/pal/mcu/PALConv2d.h
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,22 @@
/*
* Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/

#ifndef LUCI_INTERPRETER_PAL_CONV2D_H
#define LUCI_INTERPRETER_PAL_CONV2D_H
#include "PALConv2DCommon.h"

#endif // LUCI_INTERPRETER_PAL_CONV2D_H
107 changes: 107 additions & 0 deletions onert-micro/onert-micro/include/test_models/conv2d/FloatConv2DKernel.h
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,107 @@
/*
* Copyright (c) 2024 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.
*/

#ifndef ONERT_MICRO_TEST_MODELS_CONV_2D_KERNEL_FLOAT_H
#define ONERT_MICRO_TEST_MODELS_CONV_2D_KERNEL_FLOAT_H

#include "TestDataConv2DBase.h"

namespace onert_micro
{
namespace test_model
{
namespace conv2d_float
{

/*
* Conv2D Kernel:
*
* Input(1, 4, 3, 2) Weight(1, 2, 2, 2) Bias(2)
* \ | /
* \ | /
* FullyConnected
* |
* Output(1, 2, 2, 2)
*/

const unsigned char test_kernel_model_circle[] = {
0x18, 0x00, 0x00, 0x00, 0x43, 0x49, 0x52, 0x30, 0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x9c, 0x00, 0x00, 0x00, 0x10, 0x02, 0x00, 0x00, 0x2c, 0x02, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
0x88, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0xea, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x80, 0x3f, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f,
0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x40, 0xc0, 0x00, 0x00, 0x80, 0xc0, 0x00, 0x00, 0xa0, 0xc0,
0x00, 0x00, 0xc0, 0x40, 0x00, 0x00, 0xe0, 0xc0, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x80, 0x40,
0x00, 0x00, 0x00, 0xc0, 0x00, 0x00, 0x40, 0x40, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0xc1,
0x00, 0x00, 0xc0, 0xc0, 0x00, 0x00, 0xe0, 0x40, 0x00, 0x00, 0xa0, 0x40, 0xf8, 0xff, 0xff, 0xff,
0xfc, 0xff, 0xff, 0xff, 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00,
0x08, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
0x7c, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x08, 0x00,
0x0e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x18, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00,
0x2c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00, 0x13, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x07, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x98, 0x00, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x84, 0xff, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x6f, 0x66, 0x6d, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb0, 0xff, 0xff, 0xff,
0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x62, 0x69, 0x61, 0x73, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0xd4, 0xff, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x6b, 0x65, 0x72, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x10, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x69, 0x66, 0x6d, 0x00,
0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x0c, 0x00,
0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x03, 0x11, 0x00, 0x00, 0x00, 0x4f, 0x4e, 0x45, 0x2d, 0x74, 0x66, 0x6c, 0x69,
0x74, 0x65, 0x32, 0x63, 0x69, 0x72, 0x63, 0x6c, 0x65, 0x00, 0x00, 0x00};

const std::vector<float> input_data = {
18.776451, 25.97969, -9.277071, -3.5493946, 12.334248, 5.50226, -2.224743, -7.2292213,
10.259663, -1.0846977, 15.823856, 3.3193378, 4.9413986, 4.3529205, -10.353054, 3.7166824,
27.324902, -6.2231064, 10.370632, 22.661959, 20.206001, 8.245907, 9.984943, 21.379955};

const std::vector<float> reference_output_data = {1.0177879, 128.43202, 0.0, 55.28556,
39.483513, 0.0, 0.0, 7.0231743};

} // namespace conv2d_float

class TestDataFloatConv2D : public TestDataConv2DBase<float>
{
public:
TestDataFloatConv2D()
{
_input_data = conv2d_float::input_data;
_reference_output_data = conv2d_float::reference_output_data;
_test_kernel_model_circle = conv2d_float::test_kernel_model_circle;
}

~TestDataFloatConv2D() override = default;
};

} // namespace test_model
} // namespace onert_micro

#endif // ONERT_MICRO_TEST_MODELS_CONV_2D_KERNEL_FLOAT_H
Loading
Loading