Class DataProcessor

class DataProcessor

Data Processor class that processes operations. Currently supports CPU based operations.

Public Functions

inline DataProcessor()

Default Constructor.

InferStatus initialize(const MultiMappings &process_operations, const std::string config_path)

Checks the validity of supported operations.

Parameters
  • process_operationsMap where tensor name is the key, and operations to perform on the tensor as vector of strings. Each value in the vector of strings is the supported operation.

  • config_path – Path to the processing configuration settings

Returns

InferStatus with appropriate code and message

InferStatus process_operation(const std::string &operation, const std::vector<int> &in_dims, const void *in_data, std::vector<int64_t> &processed_dims, DataMap &processed_data_map, const std::vector<std::string> &output_tensors, const std::vector<std::string> &custom_strings)

Executes an operation via function callback. (Currently CPU based)

Parameters
  • operation – Operation to perform. Refer to user docs for a list of supported operations

  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

  • processed_dims – Dimension of the output tensor, is populated during the processing

  • processed_data_map – Output data map, that will be populated

  • output_tensorsTensor names to be populated in the out_data_map

  • custom_strings – Strings to display for custom print operations

Returns

InferStatus with appropriate code and message

InferStatus process_transform(const std::string &transform, const std::string &key, const std::map<std::string, void*> &indata, const std::map<std::string, std::vector<int>> &indim, DataMap &processed_data, DimType &processed_dims)

Executes a transform via function callback. (Currently CPU based)

Parameters
  • transform – Data transform operation to perform.

  • key – String identifier for the transform

  • indataMap with key as tensor name and value as data buffer

  • indimsMap with key as tensor name and value as dimension of the input tensor

  • processed_data – Output data map, that will be populated

  • processed_dims – Dimension of the output tensor, is populated during the processing

Returns

InferStatus with appropriate code and message

InferStatus compute_max_per_channel_cpu(const std::vector<int> &in_dims, const void *in_data, std::vector<int64_t> &out_dims, DataMap &out_data_map, const std::vector<std::string> &output_tensors)

Computes max per channel in input data and scales it to [0, 1]. (CPU based)

Parameters
  • operation – Operation to perform. Refer to user docs for a list of supported operations

  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

  • out_dims – Dimension of the output tensor

  • out_data_map – Output data buffer map

  • output_tensors – Output tensor names, used to populate out_data_map

InferStatus scale_intensity_cpu(const std::vector<int> &in_dims, const void *in_data, std::vector<int64_t> &out_dims, DataMap &out_data_map, const std::vector<std::string> &output_tensors)

Scales intensity using min-max values and histogram. (CPU based)

Parameters
  • operation – Operation to perform. Refer to user docs for a list of supported operations

  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

  • out_dims – Dimension of the output tensor

  • out_data_map – Output data buffer map

  • output_tensors – Output tensor names, used to populate out_data_map

InferStatus print_results(const std::vector<int> &in_dims, const void *in_data)

Print data in the input buffer in float32. Ideally to be used by classification models.

Parameters
  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

InferStatus print_results_int32(const std::vector<int> &in_dims, const void *in_data)

Print data in the input buffer in int32 form. Ideally to be used by classification models.

Parameters
  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

InferStatus print_custom_binary_classification(const std::vector<int> &in_dims, const void *in_data, const std::vector<std::string> &custom_strings)

Print custom text for binary classification results in the input buffer.

Parameters
  • in_dims – Dimension of the input tensor

  • in_data – Input data buffer

  • custom_strings – Strings to display for custom print operations

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