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
|
/*
* Copyright (c) 2018-2020 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.
*/
#ifndef ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE
#define ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/ROIAlignLayer.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
class ROIAlignLayerGenericFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
{
_rois_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::QASYMM16 : data_type;
_target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo);
_reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo);
}
protected:
template <typename U>
void fill(U &&tensor)
{
library->fill_tensor_uniform(tensor, 0);
}
template <typename U>
void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW)
{
const size_t values_per_roi = rois_shape.x();
const size_t num_rois = rois_shape.y();
std::mt19937 gen(library->seed());
TRois *rois_ptr = static_cast<TRois *>(rois.data());
const float pool_width = pool_info.pooled_width();
const float pool_height = pool_info.pooled_height();
const float roi_scale = pool_info.spatial_scale();
// Calculate distribution bounds
const auto scaled_width = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
const auto min_width = static_cast<float>(pool_width / roi_scale);
const auto min_height = static_cast<float>(pool_height / roi_scale);
// Create distributions
std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
std::uniform_int_distribution<> dist_x1(0, scaled_width);
std::uniform_int_distribution<> dist_y1(0, scaled_height);
std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
std::uniform_int_distribution<> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));
for(unsigned int pw = 0; pw < num_rois; ++pw)
{
const auto batch_idx = dist_batch(gen);
const auto x1 = dist_x1(gen);
const auto y1 = dist_y1(gen);
const auto x2 = x1 + dist_w(gen);
const auto y2 = y1 + dist_h(gen);
rois_ptr[values_per_roi * pw] = batch_idx;
if(rois.data_type() == DataType::QASYMM16)
{
rois_ptr[values_per_roi * pw + 1] = quantize_qasymm16(static_cast<float>(x1), rois.quantization_info());
rois_ptr[values_per_roi * pw + 2] = quantize_qasymm16(static_cast<float>(y1), rois.quantization_info());
rois_ptr[values_per_roi * pw + 3] = quantize_qasymm16(static_cast<float>(x2), rois.quantization_info());
rois_ptr[values_per_roi * pw + 4] = quantize_qasymm16(static_cast<float>(y2), rois.quantization_info());
}
else
{
rois_ptr[values_per_roi * pw + 1] = static_cast<TRois>(x1);
rois_ptr[values_per_roi * pw + 2] = static_cast<TRois>(y1);
rois_ptr[values_per_roi * pw + 3] = static_cast<TRois>(x2);
rois_ptr[values_per_roi * pw + 4] = static_cast<TRois>(y2);
}
}
}
TensorType compute_target(TensorShape input_shape,
DataType data_type,
DataLayout data_layout,
const ROIPoolingLayerInfo &pool_info,
const TensorShape rois_shape,
const QuantizationInfo &qinfo,
const QuantizationInfo &output_qinfo)
{
if(data_layout == DataLayout::NHWC)
{
permute(input_shape, PermutationVector(2U, 0U, 1U));
}
const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout);
TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);
const TensorShape dst_shape = misc::shape_calculator::compute_roi_align_shape(*(src.info()), *(rois_tensor.info()), pool_info);
TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);
// Create and configure function
FunctionType roi_align_layer;
roi_align_layer.configure(&src, &rois_tensor, &dst, pool_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
rois_tensor.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src));
generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout);
// Compute function
roi_align_layer.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape,
DataType data_type,
const ROIPoolingLayerInfo &pool_info,
const TensorShape rois_shape,
const QuantizationInfo &qinfo,
const QuantizationInfo &output_qinfo)
{
// Create reference tensor
SimpleTensor<T> src{ input_shape, data_type, 1, qinfo };
const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
SimpleTensor<TRois> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo };
// Fill reference tensor
fill(src);
generate_rois(rois_tensor, input_shape, pool_info, rois_shape);
return reference::roi_align_layer(src, rois_tensor, pool_info, output_qinfo);
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _rois_data_type{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
class ROIAlignLayerFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>
{
public:
template <typename...>
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
{
ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
QuantizationInfo(), QuantizationInfo());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
class ROIAlignLayerQuantizedFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>
{
public:
template <typename...>
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type,
DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
{
ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>::setup(input_shape, pool_info, rois_shape,
data_type, data_layout, qinfo, output_qinfo);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE */
|
