1 /*
2 * Copyright (c) 2018-2020 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/runtime/NEON/functions/NEReduceMean.h"
25
26 #include "arm_compute/core/Error.h"
27 #include "arm_compute/core/Helpers.h"
28 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
29 #include "arm_compute/runtime/NEON/NEScheduler.h"
30 #include "src/core/CPP/Validate.h"
31 #include "src/core/NEON/kernels/NEReductionOperationKernel.h"
32 #include "src/core/helpers/AutoConfiguration.h"
33
34 namespace arm_compute
35 {
36 namespace
37 {
validate_config(const ITensorInfo * input,const Coordinates & reduction_axis,bool keep_dims,const ITensorInfo * output)38 Status validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
39 {
40 ARM_COMPUTE_UNUSED(keep_dims);
41 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
42 ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
43 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
44 ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1);
45 ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
46
47 const unsigned int reduction_ops = reduction_axis.num_dimensions();
48 const int input_dims = input->num_dimensions();
49 Coordinates axis_local = reduction_axis;
50
51 for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i)
52 {
53 //axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)).
54 ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast<int>(input->num_dimensions())));
55 ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast<int>(input->num_dimensions()));
56 }
57
58 if(output->tensor_shape().total_size() != 0)
59 {
60 // Only validate if not using auto_init for the output tensor
61 TensorShape out_shape = input->tensor_shape();
62 // Validate output_shape only if not using auto_init
63 convert_negative_axis(axis_local, input_dims);
64 std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
65 for(unsigned int i = 0; i < reduction_ops; ++i)
66 {
67 ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
68 ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1);
69 if(output->total_size() > 0 && keep_dims)
70 {
71 ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
72 }
73 if(keep_dims)
74 {
75 out_shape.set(axis_local[i], 1);
76 }
77 else
78 {
79 ARM_COMPUTE_RETURN_ERROR_ON(i > static_cast<unsigned int>(axis_local[i]));
80 const unsigned int remove_index = axis_local[i] - i;
81 ARM_COMPUTE_RETURN_ERROR_ON(remove_index >= out_shape.num_dimensions());
82 out_shape.remove_dimension(remove_index);
83 }
84 }
85 const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
86 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
87 const bool requant = is_data_type_quantized(input->data_type()) && input->quantization_info() != output->quantization_info();
88 if(requant)
89 {
90 TensorInfo input_no_quant(input->clone()->set_data_type(DataType::F32));
91 NEDequantizationLayer::validate(input, &input_no_quant);
92 TensorInfo output_no_quant(output->clone()->set_data_type(DataType::F32));
93 NEQuantizationLayer::validate(&output_no_quant, output);
94 }
95 }
96 return Status{};
97 }
98 } // namespace
99
100 NEReduceMean::~NEReduceMean() = default;
101
NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)102 NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
103 : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _dequant(), _requant(), _reduction_ops(), _keep_dims(), _do_requant(), _input_no_quant(),
104 _output_no_quant()
105 {
106 }
107
validate(const ITensorInfo * input,const Coordinates & reduction_axis,bool keep_dims,const ITensorInfo * output)108 Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
109 {
110 return validate_config(input, reduction_axis, keep_dims, output);
111 }
112
configure(ITensor * input,const Coordinates & reduction_axis,bool keep_dims,ITensor * output)113 void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output)
114 {
115 // Perform validate step
116 ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info()));
117 // Output auto inizialitation if not yet initialized
118 const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input->info(), reduction_axis, keep_dims);
119 auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
120
121 _do_requant = is_data_type_quantized(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info();
122 _reduction_ops = reduction_axis.num_dimensions();
123 _reduction_kernels.resize(_reduction_ops);
124 _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
125 _keep_dims = keep_dims;
126
127 ITensor *tmp_input = input;
128 ITensor *tmp_output = output;
129 if(_do_requant)
130 {
131 _memory_group.manage(&_input_no_quant);
132 _memory_group.manage(&_output_no_quant);
133 TensorInfo output_no_quant_info = input->info()->clone()->set_tensor_shape(output_shape);
134 output_no_quant_info.set_data_type(DataType::F32);
135 auto_init_if_empty(*_output_no_quant.info(), output_no_quant_info);
136 auto_init_if_empty(*_input_no_quant.info(), input->info()->clone()->set_data_type(DataType::F32));
137 _dequant.configure(input, &_input_no_quant);
138 tmp_input = &_input_no_quant;
139 tmp_output = &_output_no_quant;
140 }
141
142 Coordinates axis_local = reduction_axis;
143 const int input_dims = tmp_input->info()->num_dimensions();
144
145 convert_negative_axis(axis_local, input_dims);
146
147 // Perform reduction for every axis
148 for(int i = 0; i < _reduction_ops; ++i)
149 {
150 TensorShape out_shape = i == 0 ? tmp_input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
151 out_shape.set(axis_local[i], 1);
152 auto in = (i == 0) ? tmp_input : (&_reduced_outs[i - 1]);
153
154 if(i == _reduction_ops - 1 && keep_dims)
155 {
156 _reduction_kernels[i].configure(in, tmp_output, axis_local[i], ReductionOperation::MEAN_SUM);
157 }
158 else
159 {
160 _reduced_outs[i].allocator()->init(TensorInfo(out_shape, tmp_input->info()->num_channels(), tmp_input->info()->data_type(), tmp_input->info()->quantization_info()));
161 _memory_group.manage(&_reduced_outs[i]);
162 _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], ReductionOperation::MEAN_SUM);
163 }
164 }
165
166 // Allocate intermediate tensors
167 for(int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
168 {
169 _reduced_outs[i].allocator()->allocate();
170 }
171
172 // Configure reshape layer if we want to drop the dimensions
173 if(!keep_dims)
174 {
175 TensorShape out_shape = tmp_input->info()->tensor_shape();
176 // We have to sort the reduction axis vectors in order for remove_dimension
177 // to work properly
178 std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
179 for(int i = 0; i < _reduction_ops; ++i)
180 {
181 out_shape.remove_dimension(axis_local[i] - i);
182 }
183 auto_init_if_empty(*tmp_output->info(), tmp_input->info()->clone()->set_tensor_shape(out_shape));
184 _reshape.configure(&_reduced_outs[_reduction_ops - 1], tmp_output);
185 }
186 if(_do_requant)
187 {
188 _requant.configure(&_output_no_quant, output);
189 _input_no_quant.allocator()->allocate();
190 _output_no_quant.allocator()->allocate();
191 }
192 }
193
run()194 void NEReduceMean::run()
195 {
196 MemoryGroupResourceScope scope_mg(_memory_group);
197 if(_do_requant)
198 {
199 _dequant.run();
200 }
201 for(auto &kernel : _reduction_kernels)
202 {
203 kernel.run();
204 }
205 if(!_keep_dims)
206 {
207 _reshape.run();
208 }
209 if(_do_requant)
210 {
211 _requant.run();
212 }
213 }
214 } // namespace arm_compute
215