 Here's the Python code based on your instructions. I've used the paz library APIs as you've mentioned. Please note that you'll need to install the paz library before running this code.

```python
import numpy as np
import paz

class PreprocessBoxes:
    def __init__(self, n_classes):
        self.n_classes = n_classes

    def preprocess(self, boxes, labels):
        encoded_boxes, labels = paz.get_heatmaps_and_tags(boxes, labels, self.n_classes)
        one_hot_labels = paz.test_controlmap_reduction_and_selection_to_arg_1(labels, self.n_classes)
        return encoded_boxes, one_hot_labels

class PreprocessImage:
    def __init__(self, size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
        self.size = size
        self.mean = mean
        self.std = std

    def preprocess(self, image):
        image = paz.split_and_normalize_alpha_channel(image)
        image = paz.test_controlmap_reduction_and_retention(image, self.mean, self.std)
        return paz.test_deterministic_and_stochastic_in_sequential_processor(image, self.size)

class AugmentImage:
    def __init__(self, size, crop_size, background, p_blur, p_brightness, p_contrast, p_saturation, p_hue):
        self.size = size
        self.crop_size = crop_size
        self.background = background
        self.p_blur = p_blur
        self.p_brightness = p_brightness
        self.p_contrast = p_contrast
        self.p_saturation = p_saturation
        self.p_hue = p_hue

    def augment(self, image):
        image = paz.test_controlmap_reduction_and_selection_to_arg_2(image, self.size)
        image = paz.RandomImageCrop(self.crop_size)(image)
        if self.p_blur:
            image = paz.random_image_blur()(image)
        if self.p_brightness:
            image = paz.random_image_quality(brightness_range=(0.8, 1.2))(image)
        if self.p_contrast:
            image = paz.random_image_quality(contrast_range=(0.8, 1.2))(image)
        if self.p_saturation:
            image = paz.random_image_quality(saturation_range=(0.8, 1.2))(image)
        if self.p_hue:
            image = paz.random_image_quality(hue_range=(-0.1, 0.1))(image)
        image = paz.test_controlmap_reduction_and_blend(image, self.background)
        return image

class AugmentBoxes:
    def __init__(self, expand_max_offset, p_crop, p_flip):
        self.expand_max_offset = expand_max_offset
        self.p_crop = p_crop
        self.p_flip = p_flip

    def augment(self, boxes):
        boxes = paz.ToImageBoxCoordinates(boxes)
        boxes = paz.test_controlmap_reduction_and_selection_to_arg_2(boxes, self.expand_max_offset)
        if self.p_crop:
            boxes = paz.RandomImageCrop(self.expand_max_offset)(boxes)
        if self.p_flip:
            boxes = paz.test_controlmap_reduction_and_flip(boxes)
        return boxes

class DrawBoxData2D:
    def __call__(self, image, boxes, labels, scores=None, classes=None):
        for box, label, score, class_ in zip(boxes, labels, scores, classes):
            paz.draw_box(image, box, label, score, class_)
        return image

class ShowBoxes:
    def __init__(self, size):
        self.size = size

    def __call__(self, image, boxes, labels, scores=None, classes=None):
        image = paz.test_controlmap_reduction_and_retention(image, (0, 0, 0))
        image = paz.test_deterministic_and_stochastic_in_sequential_processor(image, self.size)
        boxes = paz.ToImageBoxCoordinates(boxes)
        boxes = paz.test_controlmap_reduction_and_selection_to_arg_2(boxes, self.size)
        boxes, labels = paz._compute_bounding_boxes(boxes, labels)
        for box, label, score, class_ in zip(boxes, labels, scores, classes):
            paz.draw_box(image, box, label, score, class_)
        return image

class AugmentDetection:
    def __init__(self, preprocess_image, augment_image, augment_boxes, preprocess_boxes, show_boxes):
        self.preprocess_image = preprocess_image
        self.augment_image = augment_image
        self.augment_boxes = augment_boxes
        self.preprocess_boxes = preprocess_boxes
        self.show_boxes = show_boxes

    def __call__(self, image, boxes, labels):
        if paz.test_deterministic_and_stochastic_in_sequential_processor(image, True):
            image = self.augment_image(self.preprocess_image(image))
            boxes = self.augment_boxes(boxes)
        boxes, labels = self.preprocess_boxes(boxes, labels)
        image = self.show_boxes(image, boxes, labels)
        return image, boxes, labels

import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms

def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = ...  # Load your model here
    dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([
        transforms.RandomHorizontalFlip(),
        transforms.RandomCrop(32, padding=4),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ]))

    preprocess_image = PreprocessImage(size=32)
    augment_image = AugmentImage(size=32, crop_size=28, background=(128, 128, 128), p_blur=0.5, p_brightness=0.5, p_contrast=0.5, p_saturation=0.5, p_hue=0.1)
    augment_boxes = AugmentBoxes(expand_max_offset=4, p_crop=0.5, p_flip=0.5)
    preprocess_boxes = PreprocessBoxes(n_classes=10)
    show_boxes = ShowBoxes(size=64)
    augment_detection = AugmentDetection(preprocess_image, augment_image, augment_boxes, preprocess_boxes, show_boxes)

    for data, target in dataset:
        data, target = data.to(device), target.to(device)
        data, target = augment_detection(data, target)
        print(data.shape, target.shape)

if __name__ == "__main__":
    main()
```

Please replace the `...` in the `main()` function with your actual model loading code. This example uses the CIFAR-10 dataset and a simple data augmentation pipeline. You can adjust the parameters of the `AugmentImage` and `AugmentBoxes` classes to fit your specific needs.