Compute Library
 21.02
NEReduceMean.cpp
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25 
26 #include "arm_compute/core/Error.h"
30 #include "src/core/CPP/Validate.h"
33 
34 namespace arm_compute
35 {
36 namespace
37 {
38 Status validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
39 {
40  ARM_COMPUTE_UNUSED(keep_dims);
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);
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 
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 
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 
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
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 
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
bool is_data_type_quantized(DataType dt)
Check if a given data type is of quantized type.
Definition: Utils.h:1168
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Shape of a tensor.
Definition: TensorShape.h:39
void remove_dimension(size_t n)
Accessor to remove the dimension n from the tensor shape.
Definition: TensorShape.h:111
void run() override final
Run the kernels contained in the function.
#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)
Definition: Validate.h:108
virtual DataType data_type() const =0
Data type used for each element of the tensor.
void configure(const ITensor *input, ITensor *output)
Configure the kernel.
1 channel, 1 F32 per channel
ITensorInfo & set_data_type(DataType data_type) override
Set the data type to the specified value.
Definition: TensorInfo.cpp:321
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Status class.
Definition: Error.h:52
~NEReduceMean()
Default destructor.
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
Interface for Neon tensor.
Definition: ITensor.h:36
Copyright (c) 2017-2021 Arm Limited.
1 channel, 1 F16 per channel
TensorAllocator * allocator()
Return a pointer to the tensor&#39;s allocator.
Definition: Tensor.cpp:48
ITensorInfo * info() const override
Interface to be implemented by the child class to return the tensor&#39;s metadata.
Definition: Tensor.cpp:33
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:163
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
Definition: MemoryGroup.h:79
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEDequantizationLayer.
TensorShape calculate_reduce_mean_shape(ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims)
Calculate the output tensor shape for the reduce mean operation.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
quantized, asymmetric fixed-point 8-bit number unsigned
Coordinates of an item.
Definition: Coordinates.h:37
void allocate() override
Allocate size specified by TensorInfo of CPU memory.
bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())
Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...
void configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output)
Configure kernel.
virtual std::unique_ptr< T > clone() const =0
Provide a clone of the current object of class T.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
std::array< T, num_max_dimensions >::iterator begin()
Returns a read/write iterator that points to the first element in the dimension array.
Definition: Dimensions.h:215
void configure(const ITensor *input, ITensor *output)
Set the input and output tensors.
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
NEReduceMean(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Constructor.
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:443
unsigned int num_dimensions() const
Returns the effective dimensionality of the tensor.
Definition: Dimensions.h:143
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:792
void configure(const ITensor *input, ITensor *output)
Initialise the kernel&#39;s inputs and outputs.
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:45
quantized, asymmetric fixed-point 8-bit number signed
Coordinates & convert_negative_axis(Coordinates &coords, int max_value)
Convert negative coordinates to positive in the range [0, num_dims_input].
Definition: Helpers.h:241
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEQuantizationLayer.
void run() override
Run the kernels contained in the function.
virtual size_t num_channels() const =0
The number of channels for each tensor element.
TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true, bool increase_dim_unit=true)
Accessor to set the value of one of the dimensions.
Definition: TensorShape.h:79
static Status validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEReduceMean.
void run() override
Run the kernels contained in the function.