StandardizePreprocessor
Bases: DataPreprocessor
Data preprocessor that applies standardization
Examples:
Assume the data is a 3-channel image of data type float32.
You can scale the data to have a mean of 0 and a standard deviation of 1 by standardizing the data. In this example, the mean and standard deviation values from the ImageNet dataset are used.
>>> standardize_preprocessor = StandardizePreprocessor(
... mean_values=[.485, .456, .406],
... std_values=[.229, .224, .225],
... )
>>> preprocessed_data = standardize_preprocessor(data)
| PARAMETER | DESCRIPTION |
|---|---|
mean_values
|
mean values of the data (per channel)
TYPE:
|
std_values
|
standard deviation values of the data (per channel)
TYPE:
|
from_config
classmethod
Creates a standardize preprocessor from the configuration.
| PARAMETER | DESCRIPTION |
|---|---|
config
|
configuration
TYPE:
|
| RETURNS | DESCRIPTION |
|---|---|
StandardizePreprocessor
|
standardize preprocessor |
__call__
Preprocesses the data by applying standardization.
| PARAMETER | DESCRIPTION |
|---|---|
data
|
data
TYPE:
|
| RETURNS | DESCRIPTION |
|---|---|
npt.NDArray[np.float32]
|
preprocessed data |
StandardizePreprocessorConfig
Bases: pydantic.BaseModel
Configuration for the from_config class method of StandardizePreprocessor
| ATTRIBUTE | DESCRIPTION |
|---|---|
mean_values |
mean values of the data (per channel)
TYPE:
|
std_values |
standard deviation values of the data (per channel)
TYPE:
|