Preprocessing API - details

The purpose of this article is to present details on preprocessing API, such as its capabilities and post-processing.

Pre-processing Capabilities

Below is a full list of pre-processing API capabilities:

Addressing Particular Input/Output

If the model has only one input, then simple ov::preprocess::PrePostProcessor::input() will get a reference to pre-processing builder for this input (a tensor, the steps, a model):

ppp.input() // no index/name is needed if model has one input
  .preprocess().scale(50.f);

ppp.output()   // same for output
  .postprocess().convert_element_type(ov::element::u8);
# no index/name is needed if model has one input
ppp.input().preprocess().scale(50.)

# same for output
ppp.output() \
    .postprocess().convert_element_type(Type.u8)

In general, when a model has multiple inputs/outputs, each one can be addressed by a tensor name.

auto &input_image = ppp.input("image");
auto &output_result = ppp.output("result");
ppp.input('image')
ppp.output('result')

Or by it’s index.

auto &input_1 = ppp.input(1); // Gets 2nd input in a model
auto &output_1 = ppp.output(2); // Get output with index=2 (3rd one) in a model
ppp.input(1) # Gets 2nd input in a model
ppp.output(2) # Gets output with index=2 (3rd one) in a model

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Supported Pre-processing Operations

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Mean/Scale Normalization

Typical data normalization includes 2 operations for each data item: subtract mean value and divide to standard deviation. This can be done with the following code:

ppp.input("input").preprocess().mean(128).scale(127);
ppp.input('input').preprocess().mean(128).scale(127)

In Computer Vision area normalization is usually done separately for R, G, B values. To do this, layout with ‘C’ dimension shall be defined. Example:

// Suppose model's shape is {1, 3, 224, 224}
ppp.input("input").model().set_layout("NCHW"); // N=1, C=3, H=224, W=224
// Mean/Scale has 3 values which matches with C=3
ppp.input("input").preprocess()
  .mean({103.94, 116.78, 123.68}).scale({57.21, 57.45, 57.73});
# Suppose model's shape is {1, 3, 224, 224}
# N=1, C=3, H=224, W=224
ppp.input('input').model().set_layout(Layout('NCHW'))
# Mean/Scale has 3 values which matches with C=3
ppp.input('input').preprocess() \
    .mean([103.94, 116.78, 123.68]).scale([57.21, 57.45, 57.73])

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Converting Precision

In Computer Vision, the image is represented by an array of unsigned 8-bit integer values (for each color), but the model accepts floating point tensors.

To integrate precision conversion into an execution graph as a pre-processing step:

// First define data type for your tensor
ppp.input("input").tensor().set_element_type(ov::element::u8);

// Then define preprocessing step
ppp.input("input").preprocess().convert_element_type(ov::element::f32);

// If conversion is needed to `model's` element type, 'f32' can be omitted
ppp.input("input").preprocess().convert_element_type();
# First define data type for your tensor
ppp.input('input').tensor().set_element_type(Type.u8)

# Then define preprocessing step
ppp.input('input').preprocess().convert_element_type(Type.f32)

# If conversion is needed to `model's` element type, 'f32' can be omitted
ppp.input('input').preprocess().convert_element_type()

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Converting layout (transposing)

Transposing of matrices/tensors is a typical operation in Deep Learning - you may have a BMP image 640x480, which is an array of {480, 640, 3} elements, but Deep Learning model can require input with shape {1, 3, 480, 640}.

Conversion can be done implicitly, using the layout of a user’s tensor and the layout of an original model.

// First define layout for your tensor
ppp.input("input").tensor().set_layout("NHWC");

// Then define layout of model
ppp.input("input").model().set_layout("NCHW");

std::cout << ppp; // Will print 'implicit layout conversion step'
# First define layout for your tensor
ppp.input('input').tensor().set_layout(Layout('NHWC'))

# Then define layout of model
ppp.input('input').model().set_layout(Layout('NCHW'))

print(ppp)  # Will print 'implicit layout conversion step'

For a manual transpose of axes without the use of a layout in the code:

ppp.input("input").tensor().set_shape({1, 480, 640, 3});
// Model expects shape {1, 3, 480, 640}
ppp.input("input").preprocess().convert_layout({0, 3, 1, 2});
// 0 -> 0; 3 -> 1; 1 -> 2; 2 -> 3
ppp.input('input').tensor().set_shape([1, 480, 640, 3])

# Model expects shape {1, 3, 480, 640}
ppp.input('input').preprocess()\
    .convert_layout([0, 3, 1, 2])
# 0 -> 0; 3 -> 1; 1 -> 2; 2 -> 3

It performs the same transpose. However, the approach where source and destination layout are used can be easier to read and understand.

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Resizing Image

Resizing an image is a typical pre-processing step for computer vision tasks. With pre-processing API, this step can also be integrated into an execution graph and performed on a target device.

To resize the input image, it is needed to define H and W dimensions of the layout

ppp.input("input").tensor().set_shape({1, 3, 960, 1280});
ppp.input("input").model().set_layout("??HW");
ppp.input("input").preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR, 480, 640);
ppp.input('input').tensor().set_shape([1, 3, 960, 1280])
ppp.input('input').model().set_layout(Layout('??HW'))
ppp.input('input').preprocess()\
    .resize(ResizeAlgorithm.RESIZE_LINEAR, 480, 640)

When original model has known spatial dimensions (width + height), target width / height can be omitted.

ppp.input("input").tensor().set_shape({1, 3, 960, 1280});
ppp.input("input").model().set_layout("??HW"); // Model accepts {1, 3, 480, 640} shape
// Resize to model's dimension
ppp.input("input").preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
ppp.input('input').tensor().set_shape([1, 3, 960, 1280])
# Model accepts {1, 3, 480, 640} shape, thus last dimensions are 'H' and 'W'
ppp.input('input').model().set_layout(Layout('??HW'))
# Resize to model's dimension
ppp.input('input').preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)

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Color Conversion

Typical use case is to reverse color channels from RGB to BGR and vice versa. To do this, specify source color format in tensor section and perform convert_color pre-processing operation. In the example below, a BGR image needs to be converted to RGB as required for the model input.

ppp.input("input").tensor().set_color_format(ov::preprocess::ColorFormat::BGR);
ppp.input("input").preprocess().convert_color(ov::preprocess::ColorFormat::RGB);
ppp.input('input').tensor().set_color_format(ColorFormat.BGR)

ppp.input('input').preprocess().convert_color(ColorFormat.RGB)

Color Conversion - NV12/I420

Pre-processing also supports YUV-family source color formats, i.e. NV12 and I420. In advanced cases, such YUV images can be split into separate planes, e.g., for NV12 images Y-component may come from one source and UV-component from another one. Concatenating such components in user’s application manually is not a perfect solution from performance and device utilization perspectives. However, there is a way to use Pre-processing API. For such cases there are NV12_TWO_PLANES and I420_THREE_PLANES source color formats, which will split the original input into 2 or 3 inputs.

// This will split original `input` to 2 separate inputs: `input/y' and 'input/uv'
ppp.input("input").tensor().set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES);
ppp.input("input").preprocess().convert_color(ov::preprocess::ColorFormat::RGB);
std::cout << ppp;  // Dump preprocessing steps to see what will happen
# This will split original `input` to 2 separate inputs: `input/y' and 'input/uv'
ppp.input('input').tensor()\
    .set_color_format(ColorFormat.NV12_TWO_PLANES)

ppp.input('input').preprocess()\
    .convert_color(ColorFormat.RGB)
print(ppp)  # Dump preprocessing steps to see what will happen

In this example, the original input is split to input/y and input/uv inputs. You can fill input/y from one source, and input/uv from another source. Color conversion to RGB will be performed, using these sources. It is more efficient as there will be no additional copies of NV12 buffers.

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Custom Operations

Pre-processing API also allows adding custom preprocessing steps into an execution graph. The custom function accepts the current input node, applies the defined preprocessing operations, and returns a new node.

Note: Custom pre-processing function should only insert node(s) after the input. It is done during model compilation. This function will NOT be called during the execution phase. This may appear to be complicated and require knowledge of OpenVINO™ operations.

If there is a need to insert additional operations to the execution graph right after the input, like some specific crops and/or resizes - Pre-processing API can be a good choice to implement this.

ppp.input("input_image").preprocess()
   .custom([](const ov::Output<ov::Node>& node) {
       // Custom nodes can be inserted as Pre-processing steps
       return std::make_shared<ov::opset8::Abs>(node);
   });
# It is possible to insert some custom operations
import openvino.runtime.opset8 as ops
from openvino.runtime import Output
from openvino.runtime.utils.decorators import custom_preprocess_function

@custom_preprocess_function
def custom_abs(output: Output):
    # Custom nodes can be inserted as Preprocessing steps
    return ops.abs(output)

ppp.input("input_image").preprocess() \
    .custom(custom_abs)

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Post-processing

Post-processing steps can be added to model outputs. As for pre-processing, these steps will be also integrated into a graph and executed on a selected device.

Pre-processing uses the following flow: User tensor -> Steps -> Model input.

Post-processing uses the reverse: Model output -> Steps -> User tensor.

Compared to pre-processing, there are not as many operations needed for the post-processing stage. Currently, only the following post-processing operations are supported:

  • Convert a layout.

  • Convert an element type.

  • Customize operations.

Usage of these operations is similar to pre-processing. See the following example:

// Model's output has 'NCHW' layout
ppp.output("result_image").model().set_layout("NCHW");

// Set target user's tensor to U8 type + 'NHWC' layout
// Precision & layout conversions will be done implicitly
ppp.output("result_image").tensor()
   .set_layout("NHWC")
   .set_element_type(ov::element::u8);

// Also it is possible to insert some custom operations
ppp.output("result_image").postprocess()
   .custom([](const ov::Output<ov::Node>& node) {
       // Custom nodes can be inserted as Post-processing steps
       return std::make_shared<ov::opset8::Abs>(node);
   });
# Model's output has 'NCHW' layout
ppp.output('result_image').model().set_layout(Layout('NCHW'))

# Set target user's tensor to U8 type + 'NHWC' layout
# Precision & layout conversions will be done implicitly
ppp.output('result_image').tensor()\
    .set_layout(Layout("NHWC"))\
    .set_element_type(Type.u8)

# Also it is possible to insert some custom operations
import openvino.runtime.opset8 as ops
from openvino.runtime import Output
from openvino.runtime.utils.decorators import custom_preprocess_function

@custom_preprocess_function
def custom_abs(output: Output):
    # Custom nodes can be inserted as Post-processing steps
    return ops.abs(output)

ppp.output("result_image").postprocess()\
    .custom(custom_abs)

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