Optimize Preprocessing

Introduction

When input data does not fit the model input tensor perfectly, additional operations/steps are needed to transform the data to the format expected by the model. These operations are known as “preprocessing”.

Example

Consider the following standard example: deep learning model expects input with the {1, 3, 224, 224} shape, FP32 precision, RGB color channels order, and it requires data normalization (subtract mean and divide by scale factor). However, there is just a 640x480 BGR image (data is {480, 640, 3}). This means that the following operations must be performed:

  • Convert U8 buffer to FP32.

  • Transform to planar format: from {1, 480, 640, 3} to {1, 3, 480, 640}.

  • Resize image from 640x480 to 224x224.

  • Make BGR->RGB conversion as model expects RGB.

  • For each pixel, subtract mean values and divide by scale factor.

_images/preprocess_not_fit.png

Even though it is relatively easy to implement all these steps in the application code manually, before actual inference, it is also possible with the use of Preprocessing API. Advantages of using the API are:

  • Preprocessing API is easy to use.

  • Preprocessing steps will be integrated into execution graph and will be performed on selected device (CPU/GPU/etc.) rather than always being executed on CPU. This will improve selected device utilization which is always good.

Preprocessing API

Intuitively, preprocessing API consists of the following parts:

  1. Tensor - declares user data format, like shape, layout, precision, color format from actual user’s data.

  2. Steps - describes sequence of preprocessing steps which need to be applied to user data.

  3. Model - specifies model data format. Usually, precision and shape are already known for model, only additional information, like layout can be specified.

Note

Graph modifications of a model shall be performed after the model is read from a drive and before it is loaded on the actual device.

PrePostProcessor Object

The ov::preprocess::PrePostProcessor class allows specifying preprocessing and postprocessing steps for a model read from disk.

from openvino.preprocess import PrePostProcessor

ppp = PrePostProcessor(model)

 ov::Core core;
 std::shared_ptr<ov::Model> model = core.read_model(model_path);
 ov::preprocess::PrePostProcessor ppp(model);

Declare User’s Data Format

To address particular input of a model/preprocessor, use the ov::preprocess::PrePostProcessor::input(input_name) method.

from openvino.preprocess import ColorFormat
from openvino import Layout, Type
ppp.input(input_name).tensor() \
        .set_element_type(Type.u8) \
        .set_shape([1, 480, 640, 3]) \
        .set_layout(Layout('NHWC')) \
        .set_color_format(ColorFormat.BGR)

 ov::preprocess::InputInfo& input = ppp.input(input_name);
 input.tensor()
   .set_element_type(ov::element::u8)
   .set_shape({1, 480, 640, 3})
   .set_layout("NHWC")
   .set_color_format(ov::preprocess::ColorFormat::BGR);

Below is all the specified input information:

  • Precision is U8 (unsigned 8-bit integer).

  • Data represents tensor with the {1,480,640,3} shape.

  • Layout is “NHWC”. It means: height=480, width=640, channels=3.

  • Color format is BGR.

Declaring Model Layout

Model input already has information about precision and shape. Preprocessing API is not intended to modify this. The only thing that may be specified is input data layout

# `model's input` already `knows` it's shape and data type, no need to specify them here
ppp.input(input_name).model().set_layout(Layout('NCHW'))

 // `model's input` already `knows` it's shape and data type, no need to specify them here
 input.model().set_layout("NCHW");

Now, if the model input has {1,3,224,224} shape, preprocessing will be able to identify the height=224, width=224, and channels=3 of that model. The height/ width information is necessary for resize, and channels is needed for mean/scale normalization.

Preprocessing Steps

Now, the sequence of preprocessing steps can be defined:

from openvino.preprocess import ResizeAlgorithm
ppp.input(input_name).preprocess() \
    .convert_element_type(Type.f32) \
    .convert_color(ColorFormat.RGB) \
    .resize(ResizeAlgorithm.RESIZE_LINEAR) \
    .mean([100.5, 101, 101.5]) \
    .scale([50., 51., 52.])
# .convert_layout(Layout('NCHW')) # Not needed, such conversion will be added implicitly

 input.preprocess()
   .convert_element_type(ov::element::f32)
   .convert_color(ov::preprocess::ColorFormat::RGB)
   .resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR)
   .mean({100.5, 101, 101.5})
   .scale({50., 51., 52.});
   // Not needed, such conversion will be added implicitly
   // .convert_layout("NCHW");

Perform the following:

  1. Convert U8 to FP32 precision.

  2. Convert current color format from BGR to RGB.

  3. Resize to height/ width of a model. Be aware that if a model accepts dynamic size e.g., {?, 3, ?, ?}, resize will not know how to resize the picture. Therefore, in this case, target height/ width should be specified. For more details, see also the ov::preprocess::PreProcessSteps::resize().

  4. Subtract mean from each channel. In this step, color format is already RGB, so 100.5 will be subtracted from each Red component, and 101.5 will be subtracted from each Blue one.

  5. Divide each pixel data to appropriate scale value. In this example, each Red component will be divided by 50, Green by 51, and Blue by 52 respectively.

  6. Keep in mind that the last convert_layout step is commented out as it is not necessary to specify the last layout conversion. The PrePostProcessor will do such conversion automatically.

Integrating Steps into a Model

Once the preprocessing steps have been finished the model can be finally built. It is possible to display PrePostProcessor configuration for debugging purposes:

print(f'Dump preprocessor: {ppp}')
model = ppp.build()

 std::cout << "Dump preprocessor: " << ppp << std::endl;
 model = ppp.build();

The model will accept U8 input with the shape of {1, 480, 640, 3} and the BGR channel order. All conversion steps will be integrated into the execution graph. Now, model can be loaded on the device and the image can be passed to the model without any data manipulation in the application.