aboutsummaryrefslogtreecommitdiff
path: root/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
blob: f225ebde6b34df4cfe5dbeac76b80fe57e690d7c (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
/*
 * Copyright (c) 2017-2018 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/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h"

#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
#include "arm_compute/core/GLES_COMPUTE/OpenGLES.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"

#include <set>
#include <string>
#include <tuple>

using namespace arm_compute;

namespace
{
// Internal window config info
using GCPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size

void auto_init(const ITensorInfo *input, ITensorInfo *output, unsigned int pooled_w, unsigned int pooled_h)
{
    TensorShape output_shape{ input->tensor_shape() };
    output_shape.set(0, pooled_w);
    output_shape.set(1, pooled_h);

    auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
}

Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type() == PoolingType::L2),
                                    "Unsupported combination of parameters!");
    ARM_COMPUTE_RETURN_ERROR_ON(!pool_info.pad_stride_info().padding_is_symmetric());

    const bool         is_global_pooling = pool_info.is_global_pooling();
    const unsigned int pool_size         = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width;

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()),
                                    "Global pooling is supported only with rectangular inputs!");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)),
                                    "Invalid pool size and pool pad combination!");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_size().width != pool_info.pool_size().height, "Invalid Pool size, width not equal to height!");

    // Checks performed when output is configured
    if(output->total_size() != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);

        unsigned int pooled_w = 0;
        unsigned int pooled_h = 0;
        std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0),
                                                         input->dimension(1),
                                                         pool_size,
                                                         pool_size,
                                                         pool_info.pad_stride_info());
        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != pooled_w) || (output->dimension(1) != pooled_h),
                                        "Invalid output pooling dimensions!");
    }

    return Status{};
}

std::tuple<Status, Window, GCPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info)
{
    int                 pool_pad_x      = 0;
    int                 pool_pad_y      = 0;
    int                 pool_stride_x   = 0;
    int                 pool_stride_y   = 0;
    unsigned int        pooled_w        = 0;
    unsigned int        pooled_h        = 0;
    int                 pool_size       = pool_info.pool_size().width;
    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
    std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

    // Update pool size in case of global pooling
    pool_size = pool_info.is_global_pooling() ? input->dimension(0) : pool_size;

    // Check output dimensions
    std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0),
                                                     input->dimension(1),
                                                     pool_size,
                                                     pool_size,
                                                     pad_stride_info);

    auto_init(input, output, pooled_w, pooled_h);

    BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x);

    const int input_width  = input->dimension(0);
    const int input_height = input->dimension(1);

    unsigned int num_elems_processed_per_iteration = 1;

    // Create kernel
    if(pool_size == 3)
    {
        // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
        // each thread computes 4 output elements
        const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3);

        int num_elems_read_per_iteration = pool_size;

        if(input->data_type() == DataType::F32)
        {
            if(is_pool3x3_stride_le3)
            {
                // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
                num_elems_processed_per_iteration = 4;
                num_elems_read_per_iteration      = pool_size * (pool_stride_x + 1);
            }
        }
        else
        {
            if(is_pool3x3_stride_le3)
            {
                num_elems_processed_per_iteration = 4;
            }
            else
            {
                num_elems_processed_per_iteration = 2;
            }
        }

        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

        border_size.right  = std::max(upper_bound_w, pool_pad_x);
        border_size.bottom = std::max(upper_bound_h, pool_pad_y);
    }
    else // Run general case
    {
        if(input->data_type() == DataType::F32)
        {
            num_elems_processed_per_iteration = 1;
        }
        else
        {
            num_elems_processed_per_iteration = 2;
        }

        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

        border_size.right  = std::max(upper_bound_w, pool_pad_x);
        border_size.bottom = std::max(upper_bound_h, pool_pad_y);
    }
    // Configure kernel window
    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

    if(input->data_type() == DataType::F32)
    {
        AccessWindowStatic     input_access(input, -pool_pad_x, -pool_pad_y, input_width + border_size.right, input_height + border_size.bottom);
        AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
        bool                   window_changed = update_window_and_padding(win, input_access, output_access);
        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
        Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
        return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size));
    }
    else
    {
        // Calculate output right and bottom border
        const int output_width          = output->dimension(0);
        const int output_height         = output->dimension(1);
        const int output_padding_right  = ceil_to_multiple(output_width, num_elems_processed_per_iteration) - output_width;
        const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height;

        const int input_total_width    = std::max(int(input->padding().left), int(pool_pad_x)) + input_width + std::max(int(input->padding().right), int(pool_pad_x));
        const int input_padding_right  = ceil_to_multiple(input_total_width, num_elems_processed_per_iteration) - input_width - pool_pad_x;
        const int input_total_height   = std::max(int(input->padding().top), int(pool_pad_y)) + input_height + std::max(int(input->padding().bottom), int(pool_pad_y));
        const int input_padding_bottom = input_total_height - input_height - pool_pad_y;

        // Configure kernel window
        AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + input_padding_right, input_height + input_padding_bottom);
        AccessWindowStatic output_access(output, 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
        bool               window_changed = update_window_and_padding(win, input_access, output_access);
        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
        Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
        return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size));
    }
}
} // namespace

GCPoolingLayerKernel::GCPoolingLayerKernel()
    : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
{
}

BorderSize GCPoolingLayerKernel::border_size() const
{
    return _border_size;
}

void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const PoolingLayerInfo &pool_info)
{
    int                 pool_pad_x      = 0;
    int                 pool_pad_y      = 0;
    int                 pool_stride_x   = 0;
    int                 pool_stride_y   = 0;
    unsigned int        pooled_w        = 0;
    unsigned int        pooled_h        = 0;
    const PoolingType   pool_type       = pool_info.pool_type();
    int                 pool_size       = pool_info.pool_size().width;
    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
    const bool          exclude_padding = pool_info.exclude_padding();
    std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

    // Update pool size in case of global pooling
    pool_size = pool_info.is_global_pooling() ? input->info()->dimension(0) : pool_size;

    // Check output dimensions
    std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
                                                     input->info()->dimension(1),
                                                     pool_size,
                                                     pool_size,
                                                     pad_stride_info);

    auto_init(input->info(), output->info(), pooled_w, pooled_h);

    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info));

    // Set instance variables
    _input     = input;
    _output    = output;
    _pool_info = pool_info;

    // Set build options
    std::set<std::string> build_opts;
    build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
    build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
    build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
    if(input->info()->data_type() == DataType::F32)
    {
        build_opts.insert("#define DATA_TYPE_FP32");
    }
    else
    {
        build_opts.insert("#define DATA_TYPE_FP16");
    }
    if(exclude_padding)
    {
        build_opts.emplace("#define EXCLUDE_PADDING");
    }
    build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type)));
    build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x)));
    build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x))));
    build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y))));
    build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y)));
    build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x)));
    build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y)));

    // Create kernel
    if((pool_size == 2) || (pool_size == 3) || (pool_size == 7))
    {
        // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
        // each thread computes 4 output elements
        const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3);

        std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
        if(is_pool3x3_stride_le3)
        {
            build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED");
            _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
        }
        else
        {
            build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size));
            _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, build_opts));
        }
    }
    else // Run general case
    {
        build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size)));

        build_opts.insert("#define POOLING_LAYER_N");
        _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts));
    }
    // Configure kernel window
    auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info);
    ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

    IGCKernel::configure(std::get<1>(win_config));
    GCPoolingConfig pooling_config     = std::get<2>(win_config);
    _num_elems_processed_per_iteration = pooling_config.first;
    _border_size                       = pooling_config.second;
}

Status GCPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info));
    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info)));

    return Status{};
}

void GCPoolingLayerKernel::run(const Window &window)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

    unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();

    _kernel.use();

    _output->set_needs_shifting(true);

    Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ);

    Window slice         = window_collapsed.first_slice_window_3D();
    Window slice_in_orig = window_collapsed.first_slice_window_3D();

    slice.shift(Window::DimX, -(_output->info()->padding()).left);

    do
    {
        // Upsample input by pool size
        Window in_slice(slice_in_orig); // NOLINT
        in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
        in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));

        // Set inputs
        unsigned int idx = 0;
        add_3D_tensor_argument(idx, _input, 1, in_slice);
        add_3D_tensor_argument(idx, _output, 2, slice);

        _kernel.update_shader_params();
        enqueue(*this, slice);
    }
    while(window_collapsed.slide_window_slice_3D(slice) && window_collapsed.slide_window_slice_3D(slice_in_orig));
}