inference_config.py

import time
import sys
from data_gen import DataGenerator

from model.model import Thundernet as Thundernet_original
from model.model_ppm_factors import Thundernet as Thundernet_ppm

from collections import defaultdict

import thundernet_config as Thundernet_config
import numpy as np
import argparse
from glob import glob
from utils import resolution2framesize3cha, simple_iou_for_multiple_classes, image_test
import tqdm
from pathlib import Path
import matplotlib.pyplot as plt
from images_toolkit import show_two_images, overlap_image_with_label, show_x_images

# Example command: python inference_config.py --model_path C:/Users/user/Documents/Thundernet/pruebas_modelos/32_ppm/BS4_lossBCE_weights_lr_0.00013713842558297858_reg-1.1743577101671763e-05-ep-13-val_loss0.11463435739278793-train_loss0.053004469722509384-val_iou0.8959722518920898-train_iou0.9606077075004578.hdf5 --classes 2

baseline_duration = None

parser = argparse.ArgumentParser()

parser.add_argument(
    "--model_path",
    type=str,
    default=Thundernet_config.model_weights,
    help="Base directory for the hdf5 model, they are usually stored is /home/user/nas/deep_experiments/",
)

parser.add_argument(
    "--classes", type=int, default=Thundernet_config.classes, help="Number of classes. "
)

parser.add_argument(
    "--resolution",
    type=str,
    default=Thundernet_config.resolution,
    help="Input Resolution",
)


def main(
    args: list,
    model: str = "original",
    class_mappings: dict = None,
    transformations: tuple = tuple(),
    show: bool = False,
) -> None:
    """
    Perform inference in a set of images. If show=True, each prediction
    will be shown in the screen.
    Args:
     - args (list): list of parsed arguments
     - model (str): type of model. Default: "original"
     - class_mappings (dict): class mapper. Default: None
     - transformations (tuple): list of transformations to execute in the data. Default: tuple()
     - show (bool): display the predictions. Default: False
    Returns:
    - None
    """

    FLAGS: argparse.Namespace = parser.parse_args(args)

    # Get the model
    if model == "original":
        Thundernet = Thundernet_original
    elif model == "ppm":
        Thundernet = Thundernet_ppm
    else:
        raise ValueError(f"Unknown model: {model}")

    # Set class mapping
    if class_mappings is not None:
        FLAGS.classes = len(set(class_mappings.values())) + 1

    # Get the shape and the classes
    input_shape = resolution2framesize3cha(FLAGS.resolution)
    classes = FLAGS.classes

    # Initialize the model with loaded weights
    try:
        thundernet = Thundernet(
            input_shape=input_shape, resnet_trainable=False, n_classes=classes
        )
        model = thundernet.model
    except ValueError:
        if model == "ppm":
            Thundernet = Thundernet_original
        else:
            Thundernet = Thundernet_ppm

        thundernet = Thundernet(
            input_shape=input_shape, resnet_trainable=False, n_classes=classes
        )
        model = thundernet.model

    thundernet.model.load_weights(FLAGS.model_path)

    # Create dataloader for data
    dataset_dir: Path = Path(Thundernet_config.train_path).parent
    validation_generator: DataGenerator
    _, validation_generator = DataGenerator.create_generators(
        dataset_dir,
        FLAGS.classes,
        training_batch_size=1,
        validation_batch_size=1,
        to_stereo=False,
        transformations=transformations,
        class_mappings=class_mappings,
    )
    # Initilize lists to save data
    iou_aux: list = []
    iou_global: list = []
    durations: list = []

    # Iterate through the generator to get the iou metrics
    for i in tqdm.tqdm(range(len(validation_generator))):

        X, y = validation_generator[i]
        start_t = time.perf_counter()
        pred = model.predict(X)  # Shape: [1, 480, 640, 2]
        duration = time.perf_counter() - start_t
        durations.append(1000 * duration)

        pred = pred[0, :, :, :]  # Shape [480, 640, 2]

        prediction = np.argmax(pred, axis=2)  # Shape [480, 640]

        label = y[0].argmax(axis=-1) * 255

        if show:

            label_RGB = overlap_image_with_label(X, label)
            prediction_RGB = overlap_image_with_label(X, prediction)
            show_x_images(
                images=[label_RGB, prediction_RGB],
                titles=["Real", "Prediction"],
                horizontal=True,
            )

        iou_simple_iou = simple_iou_for_multiple_classes(
            y[0].argmax(axis=-1), prediction, classes
        )
        iou_global.append(iou_simple_iou)
        iou_aux = np.array(iou_global)

        name_image = validation_generator.get_item_name(i)

    for i in range(0, classes + 1):
        if classes <= 3 and i == classes:
            break
        values = iou_aux[:, i]
        values = values[~np.isnan(values)]
        print("IoU for class=", i, "is ", np.mean(values))

    durations = np.array(durations)

    print("")
    print("INFERENCE TIME")
    print(f" - Mean: {np.mean(durations)}")
    print(f" - Std: {np.std(durations)}")

    if baseline_duration:
        durations_baseline = np.load(Path(baseline_duration).open("rb"))
        diff_durations = durations - durations_baseline
        print("INFERENCE TIME WITH RESPECT TO BASELINE (ABSOLUTE)")
        print(f" - Mean: {np.mean(diff_durations)}")
        print(f" - Std: {np.std(diff_durations)}")
        increase_durations = (durations - durations_baseline) / durations_baseline
        print("INFERENCE TIME WITH RESPECT TO BASELINE (RELATIVE)")
        print(f" - Mean: {np.mean(increase_durations)}")
        print(f" - Std: {np.std(increase_durations)}")


if __name__ == "__main__":
    main(sys.argv[1:], model="ppm", class_mappings=defaultdict(int, {1: 1}))
    # main(sys.argv[1:], model="original", class_mappings=defaultdict(int, {1: 1}), show=False)