3_ControlNet/test_dataset.py

from torch.utils.data import Dataset, DataLoader
from pycocotools.coco import COCO
import os
from PIL import Image
from torchvision import transforms

class CustomCocoDataset(Dataset):
    def __init__(self, json_file, img_folder, common_transform=None):
        self.coco = COCO(json_file)
        self.img_folder = img_folder
        self.ids = list(self.coco.imgToAnns.keys())
        self.common_transform = common_transform
    
    def __len__(self):
        return len(self.ids)

    def __getitem__(self, index):
        img_id = self.ids[index]
        img_info = self.coco.loadImgs(img_id)[0]
        path = img_info['file_name']
        img_path = os.path.join(self.img_folder, path)
        image = Image.open(img_path).convert('RGB')

        # Perform a random crop
        i, j, h, w = transforms.RandomResizedCrop.get_params(
            image, scale=(0.9, 1.0), ratio=(1.0, 1.0))  # Ensure the same crop for both images
        
        cropped_image = transforms.functional.crop(image, i, j, h, w)

        # Resize to different resolutions
        jpg_image = transforms.functional.resize(cropped_image, 512, interpolation=transforms.InterpolationMode.BICUBIC)
        hint_image = transforms.functional.resize(cropped_image, 448, interpolation=transforms.InterpolationMode.BICUBIC)

        # Apply common transformations
        if self.common_transform is not None:
            jpg_image = self.common_transform(jpg_image)
            hint_image = self.common_transform(hint_image)

        ann_ids = self.coco.getAnnIds(imgIds=img_id)
        anns = self.coco.loadAnns(ann_ids)
        
        captions = [ann['caption'] for ann in anns]
        combined_caption = ' '.join(captions)
        
        return dict(jpg=jpg_image, txt=combined_caption, hint=hint_image)

class CustomCocoDataset(Dataset):
    def __init__(self, json_file, img_folder, common_transform=None):
        self.coco = COCO(json_file)
        self.img_folder = img_folder
        self.ids = list(self.coco.imgToAnns.keys())
        self.common_transform = common_transform
    
    def __len__(self):
        return len(self.ids)

    def __getitem__(self, index):
        img_id = self.ids[index]
        img_info = self.coco.loadImgs(img_id)[0]
        path = img_info['file_name']
        img_path = os.path.join(self.img_folder, path)
        image = Image.open(img_path).convert('RGB')

        # Perform a random crop
        i, j, h, w = transforms.RandomResizedCrop.get_params(
            image, scale=(0.95, 1.0), ratio=(1.0, 1.0))  # Ensure the same crop for both images
        
        cropped_image = transforms.functional.crop(image, i, j, h, w)

        # Resize to different resolutions
        jpg_image = transforms.functional.resize(cropped_image, 512, interpolation=transforms.InterpolationMode.BICUBIC)
        hint_image = transforms.functional.resize(cropped_image, 448, interpolation=transforms.InterpolationMode.BICUBIC)

        # Apply common transformations
        if self.common_transform is not None:
            jpg_image = self.common_transform(jpg_image)
            hint_image = self.common_transform(hint_image)

        ann_ids = self.coco.getAnnIds(imgIds=img_id)
        anns = self.coco.loadAnns(ann_ids)
        
        # captions = [ann['caption'] for ann in anns]
        captions = [ann['caption'].replace('\n', ' ') for ann in anns]
        combined_caption = ' '.join(captions)
        
        return dict(jpg=jpg_image, txt=combined_caption, hint=hint_image)


def main():
    # Define the common transformations
    common_transform = transforms.Compose([
        transforms.ToTensor(),  # Converts to tensor and normalizes to [0, 1]
        # transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # Normalizes to [-1, 1]
    ])

    # Instantiate the dataset
    dataset = CustomCocoDataset(
        json_file='/home/t2vg-a100-G4-1/projects/dataset/annotations/captions_train2017.json',
        img_folder='/home/t2vg-a100-G4-1/projects/dataset/train2017',
        common_transform=common_transform
    )

    # Create the DataLoader
    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)

    # Get the first batch
    for batch in dataloader:
        jpg_image = batch['jpg']

        # Print the min and max values in the image tensor
        print(f'JPG Image Min Value: {jpg_image.min().item()}')
        print(f'JPG Image Max Value: {jpg_image.max().item()}')

        # Exit after the first batch
        # break

if __name__ == "__main__":
    main()